How to become a Data Scientist in 2025

So you want to become a Data Scientist (or at least are thinking about it)? We love that!

Data Science is still one of the most in-demand and high-paying careers in 2025. Data Scientists in the US earn between $50,000 and $345,000 annually, with an average base salary of $126,490 and average total compensation of $143,407. Senior Data Scientists can even make up to $400,000 a year. Additionally, Data scientists rate their career happiness at 3.3 out of 5 stars, placing them in the top 43% of careers.

So how does one go about becoming a Data Scientist? We got you. This guide will cover everything you need to know to become a Data Scientist, including

• What really is Data Science?

• What skills do Data Scientists need?

• How to learn Data Science?

• How to build a Data Science portfolio?

• How to land your first Data Science job?

Part 1: What is Data Science?

What does a Data Scientist actually do?

The role of a Data Scientist is prevalent across many industries these days -- you can find them in technology, healthcare, finance, retail, and more.

While the role can vary from industry to industry, company to company, or even team to team, the core responsibilities of a data scientist remain largely the same. Here is what a data scientist typically does:

1. Data collection and cleaning: gathering data from multiple sources and preparing it for analysis or production

2. Exploratory Data Analysis (EDA) to uncover insights and drive business decisions

3. Building and Deploying Machine Learning Models to predict outcomes and automate decision-making.

4. Communicating insights and influencing business decisions

Why do companies hire data scientists? In the last decade or so, there has been a surge in the amount of data companies collect. Companies understand there is valuable information hidden within these rich datasets. Data scientists are hired to extract these goldmine of insights; enabling companies to run their businesses more effectively.

Data scientists can be thought of as fulfilling two primary roles:

• Back-end decision making: Analyzing data to inform business strategies

• Front-end model building: Developing production models that clients interact with

Both roles are integral to companies and are considered Data Science roles. We’ll discuss the different types of Data Science more in a later section.

So what tools and skills do Data Scientists typically use?

Data Scientists typically use some combination of these technical skills:

• Programming Languages: SQL, Python, R (less common)

• Data Visualization Tools: Tableau, PowerBI etc

• Database Management Tools: DBT, Airflow

• Cloud Platforms: AWS, Google Cloud, Azure

Soft skills are equally important for data scientists to succeed. Companies often look for:

• Collaboration across different teams and roles

• Communication (both written and verbal)

• Ownership / initiative across large business areas

• Leadership, including people leadership and project leadership

• Domain Expertise Deep expertise in specific industries or functions

Data Science vs. Data Analyst vs. ML Engineer vs. Data Engineer

There’s so much confusion around these roles these days. And honestly, it makes sense—because (1) all the job titles sound vaguely the same, and (2) companies love using them interchangeably (even when they shouldn't).

If you're scrolling through job boards or online courses and trying to figure out what's what, the best thing you can do is actually read the description. Make sure what you’re applying for or learning about is what you expect.

But as a high-level breakdown, here's what each role typically does:

Data Scientists do more technical and strategic work — like building machine learning models, driving deep-dive analyses and driving business strategy. They usually work closely with product managers or business stakeholders, and often own the company’s experimentation process.

Data Analysts tend to be more operational. They are typically the ones pulling data, cleaning it, building dashboards, setting up recurring reports, and answering stakeholder questions. Their work is the backbone of day-to-day decision-making.

ML Engineers focus on building the end-to-end process of productizing models. They build the infrastructure from ingesting data sources, developing models, and implementing and maintaining models in production.

Data Engineers are the builders of the data world. They make the work of every other Data person significantly easier. Data engineers are responsible for everything from setting up databases to designing data pipelines to managing data warehouses.

In summary, Data Scientists are uniquely positioned to connect business goals with technical solutions. They don’t just build models — they figure out which metrics matter, design experiments to test ideas, and translate data insights into strategic impact.

The Different Flavors of Data Science

This might be an oversimplification of the state of the world, given how diverse the Data Science role is. That said, this will provide a guide on the types of Data Science roles available.

Product Data Science

These folks are embedded within a product team. You’ll see this setup a lot at tech companies, where a Data Scientist is basically the data leader for a specific product. A “product” can mean a broad business area—like Uber Delivery—or a specific feature, like Instagram Stories.

Product Data Scientists usually work closely with a Product Manager and a cross-functional team, that includes Engineers, Designers, Product Marketers, and sometimes a Data Engineer. Their job is to use data to guide product strategy, measure success, and help the team make better decisions. Often, their day-to-day involves simpler analyses but with a quick turnaround time.

Machine Learning Data Science

These roles are heavier on the modeling side. As a consumer, we often come into contact with these models without knowing it. For example, the algorithms that decide which ads you see, which content shows up in your feed, or which email subject lines you’re most likely to click.

ML Data Scientists often sit at the intersection of business and Machine Learning. They understand the business needs deeply, and suggest the best types of models to build for each use case.

Research Data Science

These folks focus more on the algorithms themselves, and less on the application of algorithms. Their job isn’t to ship features—it’s to build better Machine Learning algorithms.

They’re the ones inventing new methods, writing papers, and going deep on the math. You’ll often find them with PhDs or research backgrounds, and their work tends to live in the early stages of the innovation pipeline.

Domain-specific Data Science (e.g. Marketing, Operations or Finance)

As the name suggests, these are Data Scientists with deep expertise in a specific domain. They might not build fancy ML models every day — but they know exactly how to apply data to make their part of the business run smarter.

Some examples of the work they do

• Marketing Data Scientists could optimize campaigns and attribution

• Operations Data Scientists might streamline logistics or inventory

• Finance Data Scientists can forecast revenue or detect anomalies in spending

They tend to be specialists in a specific part of a business, who use data to drive bigger impact in that business area.

If this feels overwhelming, just know that you don’t need to pick a “flavor” of Data Science and stick with it forever. A lot of people move between these roles during their careers. Once you understand how to think with data and work with ambiguity, the skills are easily transferrable between role types.

Part 2: What skills do you need?

top-data-science-skills-2025

Based on 100 current data science job openings, we extracted the top 7 skills required for data scientists in 2025. We will use this to guide the rest of this section because as Data Scientists we all love a data-driven approach

What technical skills do you need?

#1 Python

Python is the most important skill for any Data Scientist to have in 2025. It is required by pretty much every single job (86% per the analysis). In practice, Python is used for everything from cleaning data to building models to automating workflows.

If you’re learning Python for Data Science, remember: this is different from learning Python for Software Engineering. Focus on libraries like:

• pandas for data manipulation

• numpy for working with arrays

• scikit-learn for machine learning

• matplotlib and seaborn for visualization

You don’t need to learn how to build a web server or write object-oriented code from scratch. Stick to the data science Python toolkit.

#2 Machine Learning

It should not come as a surprise that Machine learning and Data Science come hand-in-hand. More than half (65%) of Data Science jobs require ML. However, most job descriptions aren’t looking for cutting-edge deep learning skills.

They’re looking for foundational ML understanding:

• Linear and logistic regression models

• Decision trees and random forests

• Clustering methods

And how to use ML models in the real-world:

• Selecting the right model for each use case, and understanding trade-offs and limitations

• Knowing how to evaluate models (accuracy, precision, recall, AUC, etc.)

• Developing new features for the model that incorporate business sense

Make sure that you have mastered these ML basics before moving on to advanced ML models, like Deep Learning, Natural Language Processing, and Computer Vision etc.

#3 SQL

SQL is the G.O.A.T — greatest of all time. 62% of job postings listed it, but in real life, almost every data scientist we know uses SQL regularly—often daily.

SQL is the primary way we extract data from databases, especially large ones. In today’s world, most companies are swimming in data and have extensive, often messy, databases. So if you want to do anything useful—like build a model or run an analysis—you need to know how to get the data out first. That’s why we need SQL.

How do SQL and Python interact with each other? SQL is what we use to extract, clean, and process our data into something usable. Python is what we use once we have that clean dataset—like modeling, statistical testing, and deeper analysis.

#4 R

R came up in 50% of job postings, but here’s the catch: R is usually listed alongside Python. Or put another way, R is never required on its own. But Python is often exclusively required, without any mention of R.

So if you’re just starting out, prioritize Python. It gives you more flexibility and more job options. But if you're in academia, healthcare, or government? R might still be worth learning—it’s still heavily used in certain industries.

#5 Data Visualization

Data Visualization is about how to tell stories with data. Specifically, telling stories that are easy-to-understand and convincing. It’s less about mastering a specific tool, like Tableau or Power BI.

When building data visualizations, most Data Scientists focus on

• Clear charts with appropriate labels

• Telling a story with your visuals

• Avoiding clutter and chartjunk

• Using visualizations to make decisions easier, not just prettier

What soft skills do you need?

Collaboration across different teams and roles Most Data Scientist roles are collaborative in nature. You’re not going to be heads-down writing code all day. As a Data Scientist, you’re constantly working with Product Managers, Engineers, Designers, Marketers — whole teams of cross-functional partners.

So being good at your job means being good at working with people. It’s important to be able to build and manage relationships with stakeholders. The more people trust you, the more they trust your analyses and insights.

Communication (both written and verbal) If you can’t explain what you did or why it matters, your work does not matter. Eek, that’s the harsh truth. Whether it be written updates, quick Slack messages, decks, one-pagers, talking through trade-offs in a meeting — a Data Scientist is always communicating. The best data scientists are great storytellers, not just great coders. They know how to capture attention and deliver a message.

Ownership / Initiative across large business areas Data Scientists are product and business leaders. You’ll be expected to take vague business problems and own broad business areas. That means spotting opportunities, defining your own scope, and delivering insights that actually help the business move. You are expected to be self-motivated and independent.

Leadership (people + project) You don’t need “manager” in your title to show leadership. Can you mentor a junior? Run a cross-functional analysis project? Speak up in a meeting when something’s off? That’s leadership.

Domain Expertise While you don’t need to be an expert in everything, but you do need to understand the space you’re working in. And you need to understand that space very well. Data is only useful if you know what to look for and why it matters. Whether it’s marketing, finance, supply chain, or health care — learn the business goals, metrics, and nuances. That’s how you go from “just another data person” to “strategic partner.”

Part 3: How to learn Data Science on any budget?

If that all sounds great to you and you’re ready to become a Data Scientist, this next section is for you.

When it comes to learning Data Science, many people experience a sense of choice paralysis due to the overwhelming number of options available. In this section, we’ll explore the spectrum of free versus paid learning opportunities, the differences between formal and informal education, and provide a roadmap for self-taught Data Science.

Let’s dive in!

Free vs. Paid Learning Paths

Learning Data Science doesn’t have to cost you a ton of money. There are plenty of free (or cheap) ways to get started—and also some pricier options that offer structure, support, and speed.

At the end of the day, it’s all about trade-offs. You’re trading money for structure, time for flexibility. And your best path depends entirely on you.

Some questions to ask yourself before deciding:

• Do you thrive with structure or need flexibility?

• Are you self-motivated, or do you need accountability?

• How sure are you that Data Science is the right path for you?

• And how fast are you trying to land that first Data Science role?

At a high level, free paths usually require more self-discipline and planning. You’ll need to come up with a learning plan and stick to it. It’s 100% doable — but it do require discipline and self-motivation.

Paid paths give you structure. You’ll often get pre-defined curriculum, learning resources or classes, curated projects, support from instructors or mentors, and some kind of credential or certificate. If your goal is speed or accountability, this can be a solid investment.

In reality, the hybrid option with both free and paid options would be best. For example, if you’re just learning to code, you might pay for a Python bootcamp but use YouTube to teach yourself SQL later. Or you might audit free Coursera classes, then pay a coach to give you personalized feedback on your projects.

Free Resources (or nearly free):

• Python for Data Science on freeCodeCamp (YouTube) – a beginner-friendly intro if you're just starting out.

• Machine Learning by Andrew Ng (Coursera - Audit option) – still one of the best intros to ML out there.

• Kaggle Courses – great for hands-on practice in Python, ML, and data cleaning

• DataCamp’s free tier – get access to some beginner courses and practice problems, like SQL

• Scikit-learn tutorials – great if you're starting to play with models and want official docs + examples.

Paid Options:

• Data Science Bootcamp by Springboard – offers 1:1 mentorship and career support

• Data Analyst Nanodegree by Udacity – more project-based, flexible pacing

• Data Science Specialization by Johns Hopkins (Coursera) – full path with certificate

• Coaching with a real Data Scientist – you can find loads of career coaches on LinkedIn, if you want personalized feedback, accountability, or help building a portfolio.

Of course, there is also the option of getting a Bachelor’s or Master’s degree in Data Science. Let’s get into this!

Should You Get a Master’s or Not?

First things first, having a Master’s degree is NOT a requirement for becoming a Data Scientist. It might help, but it’s not mandatory. I cannot emphasize this enough.

Whether or not to do a Master’s degree is a big decision. It’s also a very individualized decision. We’ve put together this list of questions to help guide your decision:

• Where are you now, and where are you trying to go? Are you pivoting into Data from a different industry? If so, A Master’s program can give you structure, credibility, and help you switch faster.

  But if you’re already in the data field and trying to level up to Data Science, real-world experience and a solid portfolio might be a cheaper and faster way to break in.

• What’s your budget—and is the ROI worth it? Some Master’s programs cost upwards of six figures. And don’t forget to add in the lost salary if you’re not working full-time. This becomes a major financial investment.

  Do the math: Will the salary increase cover the cost in a reasonable amount of time? Are there cheaper paths that get you similar results?

• Will you go full-time or part-time? Full-time programs mean stepping away from your job for a while. Alternatively, part-time programs mean juggling a lot at once.

  If you’re going the part-time route, ask yourself: Can I realistically balance work, school, and life? What am I willing to put on pause for a year or two?

• What kind of learner are you? Be honest with yourself here. Do you thrive in a structured classroom with deadlines and professors? Or are you someone who loves figuring things out on your own? If you’re self-motivated, you might not need the full Master’s experience — instead you could use a hybrid of paid & free options to learn Data Science.

• Do you need to build a professional network? Building a solid professional network is the best investment you can make for your Data Science career. It’s the greatest leverage you have for landing a job. Attending a Master’s program can help you build this network, giving you access to peers, instructors, and alumni connections. Of course, if you go the self-taught route, you can still build your professional network. You just need to be more intentional — join online communities, attend events, and actively connect with people in the field.

Self-Taught Data Science Roadmap: What to Learn First, Second, and Next

If you're going the self-taught route—first of all, hell yes. It's 100% possible, and tons of people have done it (myself included). But when you're doing it on your own, it can be difficult to know what to focus on, and in what order.

This roadmap is here to help. But this is just a guideline, and not a rulebook. We recommend that you tailor the roadmap based on the skills that you currently have and your goals. You got this!

1. Master Advanced SQL

SQL isn’t optional—it’s essential. It’s the #1 tool you’ll use to pull, clean, and prep your data. And in most real-world roles, you’ll use SQL way more than Python, so start by mastering Advanced SQL

Here’s what to focus on:

• Understanding how databases and tables actually work

• Basic commands: SELECT, FROM, WHERE, ORDER BY

• Aggregations like SUM, COUNT, AVG, GROUP BY, HAVING

• All the JOINs: LEFT, RIGHT, INNER, FULL, CROSS

• Common Table Expressions (CTEs)

• Window Functions

• Query optimization — yes, this is very important

• Defining metrics and building repeatable reporting queries

Btw, if you are looking for real-world SQL practice questions. You can find them on Interview Master.

2. Study Statistics & A/B Testing

If SQL is how you pull data, stats is how you make sense of that data. Understanding the fundamentals of statistics will help you run experiments, explain your insights, and build better models.

Focus on:

• Descriptive stats: mean, median, mode, standard deviation

• Common distributions: normal, Bernoulli, binomial, uniform, exponential

• Probability theory + Bayes’ Theorem

• Basic ML models: linear regression, decision trees, k-means clustering

• Core experimentation concepts: t-tests, z-tests, Type I/II errors

• How to design A/B tests: hypothesis formation, sample size, bias detection

3. Learn Python

Once you’re solid on SQL and stats, it’s time to bring in Python. You’ll use Python for more advanced analyses, modeling, and cleaning datasets that don’t live in a SQL database.

Python is the most sought-after Data Science skill in 2025. However, we recommend learning Python after SQL, because SQL is an easier language to master.

Get hands-on with:

• pandas for data cleaning and manipulation

• matplotlib and seaborn for plotting and visualizations

• scipy for hypothesis testing and statistical analysis

• scikit-learn for building your first machine learning models

4. Develop Product & Business Sense

You can be great at code and stats—but if you can’t tie your work to product and business goals, it’s going to be tough to stand out.

Work on:

• Learning the basics of Product Management

• Running your own mini product analysis projects

• Understanding how data supports product strategy and decision-making

• Defining good metrics and knowing how to evaluate them

This is what separates the coders from the strategic data scientists.

5. Hone Soft Skills

Soft skills are what turn good data scientists into great ones. These matter a lot—especially in interviews and cross-functional teams.

Practice:

• Clear, non-technical communication

• Collaborating with folks from product, engineering, design, etc.

• Taking initiative and leading your own projects

• Time management and prioritizing the right work

• Reflecting on your own learning and adjusting as you grow

6. Bonus Points: Learn Basic Data Engineering

You don’t need to be a full-on data engineer, but understanding how data gets stored, transformed, and moved around is really useful. Especially if you ever want to work on end-to-end projects or collaborate well with DEs.

Also, the most successful Data Scientists can do Data Engineering work. And vice versa.

Focus on:

• Data modeling: normalization, denormalization, dimension modeling

• Building simple ETL (Extract, Transform, Load) pipelines

• Data cleaning, validation, and loading strategies

• Writing tests for your data pipelines and ensuring data quality

Phew! That’s a lot to learn. But Data Science is a complex and rich field, and so, there is a lot involved.

If you’re feeling overwhelm by this learning roadmap, that’s totally understandable. The key is to take it one step at a time and go at a pace that works for you. Realistically, it can take you anywhere between 1 to 3 years to learn all of these and actually get good at it. That’s normal. That’s expected. You got this.

Finding opportunities to do Data Science work in your current role

We’ve talked a lot about learning Data Science, but Data Science is about application, not theory. The best way to actually learn Data Science? Do it. Get hands-on, practical experience.

Having worked with a lot of folks who’ve made transition into Data Science, one of the easiest and most overlooked strategies is this: start doing Data Science work where you are right now.

You don’t always need a new title or job to start building your skills. Look for ways to level up the role that you’re currently in:

• Maybe you’re a Data Analyst, and most of your job is reporting. Cool — find a project where you can run a hypothesis-driven analysis instead.

• Maybe you’re a Data Engineer building pipelines. Perfect, now use those tables you created and run an Exploratory Data Analysis to identify a strategic opportunity for the business.

• Maybe you’re in Marketing and optimizing campaigns. If you have access to the data, go deeper: design an experiment, build out the analysis yourself, and make recommendations.

That’s real-world Data Science. And it shows initiative and technical skills — two things that hiring managers love.

Ok but what if your current role doesn’t give you access to any of this? That’s okay. That’s actually where a lot of people start. And that’s exactly what the next section is for—how to get practical, hands-on experience by building a portfolio and working on real projects.

Let’s keep going.

Part 4: How to build a Data Science portfolio?

Why is having a portfolio important?

This feels obvious, but hiring managers are looking for people who can do the work. If you don’t have formal Data Science experience, a portfolio is your best way to prove that you have the required skills. Your portfolio how you show you can do the work, instead of just saying you can do it.

A strong portfolio does two things:

1. Shows that you know how to solve real business problems with data

2. Gives you something concrete to talk about in interviews (beyond just coursework)

Your projects should reflect real-world scenarios — not “school project” problems. That means thinking through a business question, doing the analysis, drawing conclusions, making recommendations, and clearly communicating your results. We’re going to talk more about this in a second.

And quick note: not everyone needs a portfolio. If you’re already in the industry doing Data Science type work, your real experience is more valuable. But if this is your first or second Data Science job, a portfolio is critical to helping you land your first Data Science job.

What hiring managers look for in a portfolio?

This might surprise you: but hiring managers don’t look at your code first. Instead, they look for something more important— your insights, recommendations and communication skills.

Why? Because no one hires a Data Analyst just to run queries or build dashboards.

They hire you to think critically about the business and to extract meaning from data. At the end of the day, your goal is to drive the business forward.

So yes, your code, tools, and technical skills matter. But they’re not the main thing.

That includes:

• How well you write up your findings?

• How you tell stories with visuals (think dashboards or slide decks)?

• How clearly your problem-solving skills come through?

So a strong portfolio boils down to just these 3 things:

1. Clear insights and recommendations – What did you find? Why does it matter?

2. Strong, succinct communication – That includes: How well you write up your findings? How you tell stories with visuals (think dashboards or slide decks)?

3. Relevance of the project to the role – Does your project showcase the skills and types of problems you'd actually work on in the role?

Project ideas for beginner, intermediate and advanced Data Scientists

Before we get into project ideas, it’s important to understand what actually separates beginner, intermediate, and advanced work.

It’s less about which tools you use, and more about

• The complexity of the problems you tackle

• How much ambiguity you can handle

• The level of business impact you drive

We recommend that you start with beginner or intermediate projects. But eventually work your way up to advanced ones — because that’s what will set you apart and get you hired for higher-level roles.

Beginner Projects: Focus on learning the basics and building confidence

Scope: Small, well-defined problems with clear outcomes. Ambiguity: Low. You can follow structured tutorials for Beginner projects. Goal: Show you understand core tools (like SQL, Excel, or Python), can clean and explore data, and answer very well-defined questions.

Examples:

• Analyze a public dataset (e.g. Netflix titles, bike share, COVID-19)

• Build a basic dashboard in Tableau or Power BI

• Write SQL queries to answer business-style questions

Intermediate Projects: Start to think like a Data Analyst on the job. Scope: Medium-sized projects that require connecting multiple concepts or datasets. Ambiguity: Moderate. You define the question, clean the data yourself, and make assumptions. Goal: Show you can handle messy real-world data, find insights, and communicate them clearly.

Examples:

• Combine multiple sources (e.g. sales and marketing data) to find patterns

• Build a KPI dashboard for a hypothetical stakeholder

• Do cohort analysis or time series trend detection

Advanced Projects: Think like a consultant or product analyst. You care about impact.

Scope: End-to-end projects with business context, self-scoped goals, and stakeholder-style recommendations. Ambiguity: High. You define the problem, make judgment calls, and justify your decisions. Goal: Prove you can drive strategic decisions with data, and communicate like a pro.

Examples:

• Simulate a company’s revenue churn problem and propose retention strategies

• Analyze product usage and recommend a feature sunset or A/B test

• Build a self-serve dashboard for execs with built-in narrative storytelling

How to present projects to the public

This is one of the most critical parts of building your portfolio — because first impressions matter.

Imagine you’re a hiring manager. Two potential hires have done the exact same project.

One throws it onto a messy GitHub repo with no context. The other presents it on a clean, well-structured website. Which candidate would you hire? The second person of course.

So how should you present your portfolio projects?

We recommend using a Notion page to host your portfolio projects. It’s very user-friendly, free and sleek. You can use this Notion portfolio template to get started.

Think about your portfolio website like an executive summary for your projects. You want to use it to highlight the most important thing — your ability to drive impact and communicate insights — assuming the recruiter or hiring manager only looks at it for 15 seconds.

Here’s how to present your projects effectively in your portfolio:

• Start with the problem you’re solving

• Highlight the key methods you used

• Clearly state the outcomes or recommendations

• Add links if the reader wants to go deeper (e.g. a link to a Tableau Public dashboard or Github for code)

How many portfolio projects should you have in your portfolio? We recommend 2 to 3 end-to-end, polished projects. When it comes to portfolio projects, quality matters more than quantity. Having just a few relevant and completed projects is better than having 15 half-baked projects.

Common Mistakes People Make in Their Portfolios

1. Focusing too much on code or methodology, not enough on insights

   Your SQL query might be complex and advanced. Your Python might be the most technical code any Data Analyst has done. But if a hiring manager can’t quickly understand why your analysis matters, none of that technical skill matters. So always lead with the insight and recommendations, not your process.

2. Doing too many projects, but none of them well

   As mentioned before, more =/= better. Ten half-done projects won’t impress anyone. Two or three thorough end-to-end projects will be far more effective.

3. Choosing generic projects that don’t reflect your interests or goals

   Analyzing Titanic survival or the Iris dataset won’t make you stand out. It’s a good place to start, but everyone has done those projects. Instead, tailor your projects to the industry you want to work in — so that in an interview, you can easily explain how your projects align with the job.

4. Forgetting that presentation matters

   Just like how you’d dress up for an interview to give a good impression, polish how your portfolio looks. By the way, as you learn more about communication and presentation. You can (and should) go back and refine older projects — this is worth your time and investment.

5. Treating it like a school assignment

   Real-world data is messy, incomplete, and ambiguous. Don’t rely solely on classroom-style projects with clean datasets and step-by-step instructions. These don’t inspire confidence that you can do the work. Instead, show you can handle the kind of messy, unstructured problems you’ll actually face on the job. It’s more work, but it’s also more valuable.

Part 5: How to land your first job?

So you’ve been working hard for the past months or years to build the right skills and your portfolio, and now you’re ready to land your first job. How do you do this?

• Craft a professional, Data Science resume

• Polish your LinkedIn profile

• Build and use your network

• Apply for jobs strategically and consistently

• Prepare for and ace your interviews

• Negotiate salary and start your first job

How to Tailor Your Resume for Data Science Roles

We could write an entire article on this section (and we probably will), but for now, let’s keep this brief but helpful.

Before diving into the details, we should underscore that your resume is not a place to list everything you’ve ever done. Rather, it’s a highlight reel — so you only want to highlight what is most important and relevant to the jobs that you’re looking for. Generally, we recommend that your resume is only 1 page max (unless you have over 10 years of experience), so it’s really critical to only highlight the most important pieces of information.

These are the core technical skills that show up in almost every single Data Scientist job description:

• Python or R: Show that you’re fluent in at least one — ideally Python. Bonus if you’ve used it for real-world projects, internships or contract work.

• SQL: Show off your ability to do complex data processing, but more importantly, how you’ve used SQL to solve real-world business problems.

• Statistics: Understanding p-values, confidence intervals, hypothesis testing, and distributions are the basic table stakes.

• Machine Learning: You don’t need to know how to build the next generation of AI models, but you should be comfortable with regression, classification, and knowing when to use what.

And of course, don’t forget to highlight soft skills. Because harsh truth: the best technical candidate still won’t get hired if they can’t work with people or explain what they’re doing.

• Communication & Collaboration: Can you explain your findings to non-technical teammates? Can you write a clear summary of your analysis? Are you able to speak through your findings in a convincing manner?

• Leadership: Leadership is not about having a manager title. It’s about how you are able to lead projects or teams to a successful outcome. That might mean leading a school club, organizing a community initiative, or stepping up to guide a team project, especially if you're just starting out in Data Science.

• Initiative: Did you take ownership of a project? Start something on your own?

• Adaptability: The Data industry is changing constantly. Demonstrate that you’re able to quickly adapt to whatever the industry throws at you.

Remember how we talked about the different flavors of data science earlier? You don’t need to completely rewrite your resume for each Data Science role type — instead, just emphasize the projects or skills that are most relevant to that specialization.

Following the flavors of Data Science that we mentioned earlier:

• For Product Data Science, you’ll want to highlight AB testing, defining and improving KPIs, cross-functional collaboration with Product Managers and Engineers.

• For Machine Learning Data Science, you’ll want to highlight end-to-end ML projects, model performance metrics (like precision, recall, or F1-score), and business impact driven by model outputs.

• For Research Data Science, you’ll want to highlight published research or papers, contributions to novel algorithms or methods, and experience with advanced modeling techniques.

• For Domain-Specific Data Science (e.g. Marketing, Operations, Finance), you’ll want to highlight domain-relevant tools or platforms, business outcomes tied to your work, and partnerships with cross-functional (often non-technical) teams.

The importance of quantifying impact on your resume

One of the most critical ways to make your resume stand out is by quantifying your impact.

Why? Because hiring managers don’t just want to know what you did. They want to know how the business improved because of your work. Did your analysis lead to a product change? Did it boost revenue or reduce churn? Did your dashboard or pipelines help a team move faster?

Always lead with outcomes, not effort. There are two main ways to showcase this:

1. [Preferred] Outcome-Based Impact: This method highlights what business metric changed because of your work.

   • Helped grow subscription revenue by 12%

   • Reduced user drop-off by 8% after a funnel analysis

   • Uncovered insights that informed the Q3 product roadmap

2. Scope-Based Impact: Sometimes, it’s not possible to measure the impact of your work based on outcome. That’s okay -- you can still show the scale of your work.

   • Analyzed 15M+ rows of event data across 4 systems

   • Built dashboards used weekly by 6 product teams

   • Managed data pipelines for 3 high-traffic user flows

Pro Tip: If you can tie your work to a number or a dollar value — do it, this is recommended. But if you can’t, that doesn’t make your work less valuable. Just talk about the scale, reach, or strategic importance instead.

Also if you're early in your career? School projects, internships, contract gigs, or volunteer work totally count — as long as they show skills and impact!

Don’t forget to link your portfolio

In the previous section, we talked extensively about why and how to build a Data Science portfolio, and we’d be remiss not to mention it here.

This is especially important if this is your first job in Data Science.

How should you include your portfolio on your resume? Add a Projects section where you briefly highlight 1–3 relevant portfolio projects. Then, include a link to your portfolio page or GitHub so recruiters and hiring managers can explore the full details.

How to Build a LinkedIn Profile That Gets Interviews

Your LinkedIn page is your professional public brand. For professional connections made online, this is often the first impression that people have of you. A recruiter, a hiring manager, an interviewer — these folks will often “look you up” before they meet you for the first time.

So it’s really important to have a professional and eye-catching LinkedIn page. Here’s a simple checklist of what to get on your LinkedIn page:

• Profile Picture Choose a clear, friendly, professional photo. You don’t have to be wearing a suit or a formal dress — just make sure your face is well-lit and you look approachable.

• Summary Section Write a short, 5-10 sentence summary that covers what you do, what tools you use, and what kinds of problems you like to solve. Think of it as a quick elevator pitch: what do you want people to know right away, that will entice them to want to learn more?

• Featured Section / Portfolio Add your top one or two portfolio projects or a link to your portfolio website. This gives people something to click into and helps them quickly understand your skill set.

• Experience Make sure each job or internship includes bullet points with impact — not just tasks. If you’ve done data work in non-Data roles, include that too! That is important experience that counts for a lot!

• Education & Certifications Add your degree(s), bootcamps, and any relevant certifications. Don’t overthink this — just make sure it’s complete and accurate.

One question we get a lot: Do I have to post on LinkedIn?

No. You don’t need to make posts on LinkedIn. But it might help you get more visibility among recruiters and hiring managers, if you’re actively job searching.

If you do feel comfortable posting occasionally — like sharing a project you finished or reflecting on something you learned — that can help expand your reach even further. But again, it’s not a requirement. Focus on what feels sustainable for you.

How to build and leverage your network

A strong professional network isn’t something you build in a week or a month. It takes years of genuinely making and investing in human connections.

If you’re serious about breaking into Data Science, don’t wait until you’re ready to apply to jobs to start networking. By then, you’re already playing catch-up. Start building your network early, because your network will pay dividends over time.

Let’s be real… Networking can feel awkward or transactional. But it doesn’t have to be, if done right. Good networking is just relationship building. It’s mutually beneficial and based on genuine curiosity and care.

Some of our best tips on building a strong professional network:

• Start with who you already know Do you have any friends, family, former classmates, or colleagues in the data space? Or someone who might know someone? Reach out to them and ask for a casual chat. You’d be surprised how many people are willing to help if you just ask.

• Attend networking events

  Whether it’s a local meetup, online panel, industry conference, or data bootcamp alumni event — put yourself in rooms (or Zooms) with people in the field.

• End every chat with an ask At the end of each conversation, try asking:"Are there one or two people in data you think I should connect with? Would you be open to making an intro?" This keeps your network growing without cold messaging strangers.

• Stay in touch Relationships fade if you don’t invest in them. Try to check-in every once in a while with your connections, so that you stay top of mind: send a quick check-in, share something relevant to their interests, or follow up if they gave you advice and it helped. It doesn't need to be constant — just make sure it’s authentic.

• Offer value first Networking isn’t just about what you can get. Strong connections are built on give-and-take. Even if you’re just starting out, you can still give. For example, you could:

  • Share a job post, article, or event you think they’d like

  • Offer a skill you do have — maybe you’re good at design, writing, or marketing

  • Help out however you can, even if it’s outside of data (watching their kids so they can attend a meetup counts)

Where to look for job openings?

Probably the most important step of this entire process is applying to job openings. It can feel overwhelming at first — like there are a million platforms, so where to begin? Pro-tip: you don’t need to be on every single platform. Focus on 1–2 job boards that align with your goals, and go deep rather than wide.

• For full-time roles, start with LinkedIn or Indeed: these are the biggest platforms and where most traditional Data Science jobs get posted.

• For startup jobs, Wellfound (previously Angellist Talent). It’s great for early-stage and tech startup roles, including data positions that may not be listed on larger job boards.

• For contract or freelance roles, look into Fiverr or Upwork, especially if you’re looking to gain project experience or build your portfolio.

Pro-tip: Most of these platforms have custom, automated alerts. They will send you an email or notification when a job related to your search gets posted. This will save you lots of time, and ensures that you'll always know of the latest opportunities.

Prepare for and ace your next interview

Interviews are where you showcase everything you’ve been working on: your skills, your experience, your communication, and your confidence… the list goes on. This is your chance to show that you’re not just qualified on paper, but ready to do the job.

The best time to start preparing for interviews? The moment you hit “apply.” Don’t wait for an invitation to start practicing. Interview prep takes time, and you’ll feel a lot more confident when you're not cramming last-minute.

That was worth saying again — start preparing for interviews early! You want to go into any interviews you have over-prepared.

These are the most common types of interviews for Data Science role. It will vary by company, and even by team. As a general rule of thumb, here are the interviews that you should be prepared for, and where you can prepare for them:

• SQL interviews: practice on Interview Master

• Coding interviews: practice on LeetCode

• Stats / ML interviews: practice on Interview Query

• Case study interviews: read Ace the Data Science interview

• Behavioral interviews: practice with a friend, mentor or career coach

General Interview Tips

Start preparing early Interview prep isn't a cram-the-night-before situation. The earlier you start practicing, the more confident you’ll be when the real thing comes around.

Practice talking about your past experience through a Data Science lens Even if your past work wasn’t in a data-specific role, talk about how you approached problems, worked with data, collaborated cross-functionally, or drew insights. Make sure to highlight both soft skills and technical thinking when you’re talking about your past experience.

Mock interviews are incredibly helpful Don’t just read — practice. In fact, practice out loud. Rehearse with a friend, a coach, or even AI tools to get lots of mock interview practice in. For example, Interview Master lets you run SQL mock interviews with immediate feedback. Not only do mock interviews help you build your interviewing skills, they also help get your nerves out before the real interview.

Don’t wing it Every interview is a valuable opportunity. Treat each one like it matters — because in this tough market, interviews can be tough to come by.

Be early and bring good energy Show up on time (or a few minutes early) for every interview to give off a good first impression. Also, remember your interviewers are people. Be engaged, be curious, and treat them like the future teammates they might be.

A Quick Note on Take-Home Assignments

It’s very common in the Data field to get a take-home assignment — a project you’ll complete over a few days in your own time, using whatever resources you have handy. This is your chance to go deep on a problem you wouldn’t be able to solve in a one-hour interview.

Take these seriously. They’re often weighted heavily in the hiring process. Go above and beyond — make your code clean, your methodology clear, and your problem-solving process explicitly. Imagine you’re already in the job that you’re interviewing for, now what kind of deliverable would your coworkers be impressed by? That needs to be the quality of your take-home assignment.

Negotiate salary and accept your job offer

So you’ve got a job offer — congrats! That’s huge

But before you accept it right away, take a breath. Give yourself a day or two to review the offer carefully and think through what matters most to you. This is your chance to make sure the role suits not just your career goals, but also your lifestyle and values.

You don’t have to take the first offer that comes your way.

A few things to keep in mind while considering whether or not to accept an offer:

• Salary isn’t the only thing you can negotiate. You can also ask about signing bonuses, equity or stock options, relocation support, paid time off, flexible hours, and professional development stipends etc, etc, etc.

• Do your research. Use platforms like Levels.fyi, Glassdoor, or Blind to get a sense of market rates for similar roles.

• Be polite but confident. Negotiation doesn’t need to be aggressive — it’s a normal part of the process. Express enthusiasm for the role while asking for what you need. Remember that the recruiter or hiring manager is incentivized to help you accept the offer, so you have some bargaining power here.

• Know your walk-away point. Not every offer will be the right fit, and that’s okay. Know your worth and what’s non-negotiable for you.

You don’t have to take the first offer that comes your way.

Once everything looks good and you feel aligned, send your acceptance with confidence.

You earned it. You are now a Data Scientist!