Are you looking to enter data science but don’t know where to start? Are you asking yourself how to become a data scientist with no experience? I have compiled a list of tips to help you land your first data science job. From step-by-step guides on getting started to tips and strategies for acing the interview, I list everything you need to secure your dream job in this competitive field. Note that this guide assumes that you already have some foundational domain knowledge.
This post covers topics such as what to display on your resume to appeal to recruiters and hiring managers, networking with industry professionals, and preparing for interviews so that you can optimize the chances of success of your job search.
Do you want to land your first data science job this year? Read my ultimate guide now and take the first step toward securing your dream data science job in 2023.
Table of Contents
What is data science?
Data science is a multidisciplinary field that uses statistical and computational techniques to extract insights and knowledge from data. It involves collecting, cleaning, and processing data from various sources to identify patterns, trends, and correlations. Data science also involves building predictive and causal models that inform business decision-making. Data scientists use various tools and technologies to analyze data, including programming languages like Python and R, databases like SQL, and data visualization tools like Tableau. Data science jobs are found in a wide range of industries, including finance, healthcare, marketing, and technology.
Why you need to be strategic when looking for your first data science job – even if you have all the right “qualifications” on paper
If you’re looking to land your first data science position, it’s important to use different strategies than more experienced candidates to prove that you have the necessary skills. Essentially, the less work experience you have, the less information prospective employers have about you. In this post, I will explain how you can showcase your abilities even without formal data science experience and how resume items seemly unrelated to data science can still be useful.
Steps to landing your first data science job
“How to become a data scientist with no experience” is one of the questions I get most frequently from students. My advice is based on things that I have seen work in the job application process for candidates who apply for entry-level data science jobs. I have interviewed many junior data scientist candidates and coached undergraduate and graduate students looking to enter the data science field. Below I list the things I have found to be crucial to persuade a recruiter that you’re a candidate worth looping in and convincing the hiring team that, even with no practical professional experience, you are a good hire.
1. Build a technical foundation – but be selective
Advice you often hear is to build a strong foundation in data science skills. It is true that to be successful in data science, you need a strong foundation in skills like statistics, programming, and machine learning. Take courses or participate in online tutorials to improve your skills and knowledge in these areas. The problem with this advice is that data science is such a broad field that it is overwhelming for an entry-level data scientist. Data science knowledge encompasses topics from data visualizations to deep learning. You’ll have time to dive into advanced topics once you start working. What is crucial is to have a solid foundation in skills that can’t be taught on the job, like statistics, programming (preferably Python), and critical thinking. What (good) employers look for in entry-level job candidates is a good enough foundation to be coachable. Remember, a large part of the success of any data science project rests on knowing how to look at the data and ask the right question. This has nothing to do with being able to program Markov Chain Monte Carlo sampler or a feed-forward neural network. If you don’t have any prior experience, the most important skills to show are the ability to critically look at and interpret data, ask questions relevant to the business problem, and have a solid foundation in statistics (this means that you need to be able to explain what a p-value is and how you calculate it), and be able to do basic data cleaning and regressions in Python (R is ok but I really recommend Python). If you come from academia, Stata is not something that will help you in the industry.
2. Don’t ignore the soft skills
Students often ask me questions like “What scientific journals should I read to optimize my job search chances” or “What other skills should I learn”. My response is usually, “You have enough technical skills to get your first job; now focus on communication”.
Even if you are exceptionally qualified, it won’t get you a job unless you can convey this. Also, there is no way you will know most things about every topic when entering your first job. The key is to focus on foundations that will let you learn on the job and convince the prospective employer that you have those foundations.
Below I list some strategies that you can use when looking for entry-level jobs.
Showcase structured thinking – learn to communicate your potential
Key aspects of good communication can be shown by structured thinking. This means that when asked a question during an interview, you don’t dive in head first into some detailed rabbit hole of an answer without first taking a step back and assessing the problem. Remember, interview questions are rarely textbook-type questions. Rather, they tend to be ambiguous on purpose. Lay out the problem first and ask clarifying questions. Then, give an overview of your solution framework before going into the details. Think out loud as you answer so that the interviewer can follow along with your train of thought, and remember to be perceptive to real-time feedback. If you go off the right track, the interviewer may try to nudge you in the right direction. However, they may be subtle, and you may miss the cues if you’re not a good listener. Btw, taking notes during the interview is totally fine.
Be prepared for “behavioral” questions
A typical interview includes at least one round of “behavioral” questions such as “Tell me about a time when you had a conflict with a coworker and how you resolved it”, or, “Tell me about a time when you had to convince a stakeholder that your solution was correct”. Make a list of situations you have experienced that have involved some sort of conflict, difficulty, ambiguity, or challenge. Then think about how you acted in these situations and the outcome. Obviously, pick situations that showcase your character strengths. It’s ok to tell about a time when you made a mistake, but there needs to be a learning aspect to the story. Learn to answer behavioral-type questions using the STAR interview method. If you don’t have an exact answer to a question, you can say something like, “I have not been in this exact situation, but if I were, this is how I would handle it”.
Practice for interviews: if you are not a super confident coder, it is good to practice some coding questions using a platform like Hackerrank. Coding questions during interviews tend to be of two types. The first one is algorithm-like questions, something along the lines of “Generate a list of random numbers between 1 and 10, then write a function to order the numbers in ascending order and, if any two adjacent numbers differ by more than 3 in absolute value, insert the average of the two values between them”. The second type of question involves actual data. You can be given a data set and asked to perform some kind of inference, such as writing a simple predictive model. For these types of questions, always look at general summary statistics first to ensure that there are no “catches” in the data, such as imbalance or missing or otherwise nonsensical values. A huge red flag is someone who goes right in and calculates the solution without looking at the data. Plots are your friend here.
Beware of companies that don’t let you use Google when doing a coding interview. Anyone who programs knows that we use Google and Stackoverflow all the time; the interview is supposed to test if you can solve a problem, not whether you remember all possible coding commands off the top of your head.
Remember that, fundamentally, people want to work with nice people. It doesn’t matter if you can derive the central limit theorem for some complicated estimator or program an extremely efficient data pipeline if you’re an asshole. Junior candidates tend to put too much emphasis on technical skills; once you’ve worked for a while, you realize how important liking the people you work with is.
3. Create tangible evidence of your skills
As a junior candidate with no prior work experience, you can do several things to stand out in the candidate pool and give prospective employers confidence in your skills. The general idea is to create a strong online presence.
Build a portfolio
Showcasing how you approach data analysis by creating a portfolio of your data science projects is a great way to advertise skills to potential employers. You can work on personal projects, such as a thesis, or contribute to open-source projects to build your portfolio. Don’t think that just your higher education is enough to land you a job.
If you’re so inclined, you can participate in Kaggle competitions: Kaggle is an online platform that hosts data science competitions where participants can work on real-world problems and even win cash prizes. Participating in Kaggle competitions can help you gain valuable experience and exposure to the data science community.
Make sure to have a well-structured GitHub repository with your projects.
Make a website
A personal website is an online business card that is part of creating your own brand. It helps you stand out among other candidates and allows you to present yourself in a personalized way. Make sure your website is good-looking – there are many tools out there that can help you build a website, even if you’ve never done this before. By making a website, you also show that you are tech-savvy and can learn skills outside the strict data science realm.
Make sure your LinkedIn looks good
LinkedIn is often the first place that recruiters look at. Do make sure that your LinkedIn looks professional. The less experience you have, the more important such things are.
- Use a professional-looking profile picture: Make sure the image is clear and high-resolution.
- Write a compelling headline: Your headline should accurately reflect your current role and showcase your unique skills and experiences.
- Write a summary that stands out: Your summary should be a short, engaging introduction to who you are and what you do. Use bullet points and clear language to highlight your skills and experiences.
- Highlight your work experience: List your work experience in reverse chronological order, including your job title, company name, and dates of employment. Use bullet points to highlight your accomplishments and responsibilities.
- Add relevant skills: Add skills that reflect your expertise, and ensure they’re relevant to your industry and job function.
- Get endorsements and recommendations: Request that colleagues and connections endorse you and write recommendations for you. It’s a great way to build your credibility while boosting visibility on LinkedIn.
- Engage with content: Like, share, and comment on relevant posts to increase your visibility and engage with your network.
- Keep your profile up-to-date: Make sure your profile is up-to-date with your latest work experience, skills, and accomplishments.
- Link to your website in your LinkedIn profile.
Make your resume relevant
Even if you don’t have any internship or job experience related to data science, think of any projects you have done in the past that showcase skills relevant to prospective employers. Those can be technical skills or soft skills. For example, if you were a star college athlete, this shows that you are committed to hard work and discipline. If you led a reading group, this shows that you are (hopefully) a good communicator. In general, highlight evidence that you have good communication skills and can cooperate with others.
If you have done any projects for technical courses, describe them on your resume in a way that conveys “this is what I learned, and this is why it’s useful”. For example, don’t write “Final programming assignment for Machine Learning class”. Instead, write, “Maximizing business revenue using Machine Learning: identified an optimal promotion strategy using a promotion and sales data set from an online merchant with 100,000 rows and 100+ features.” You get the idea.
Spell-check all of the above!
Ensure you don’t have typos in your resume, LinkedIn profile, or personal website. I recommend using a tool such as Grammarly. If your English is not perfect, ask someone qualified to double-check your stuff.
4. Network, network, network
Your network is your net worth, the saying goes. I have found this to be true. Attend data science events, meetups, and conferences to network with professionals in the field. Don’t be afraid to walk up to people and introduce yourself. Connect with data science professionals on social media platforms like LinkedIn and Twitter.
A good way to network is to reach out to someone on LinkedIn with a specific question they can help with and ask for a brief (15 min!) informational conversation. Most senior people like to help junior people. When you reach out, keep your message brief and to the point. Think: “Dear X, I’m a 4th-year undergraduate student in Data Science, and I am currently working on a project about customer segmentation using Machine Learning. I am looking to learn more about this type of work in the tech industry, and I wonder if you would have time for a 15 min virtual coffee so that I could ask you a few questions about your experience working in this field”. The worst thing that can happen is that someone doesn’t answer or says no. Write to many people. When you talk to them, stick to the 15 min timeline and TAKE NOTES.
5. Apply for the right jobs
Read the job description and understand what it says
Ensure you read the job posting and only apply if you meet at least 70% of the requirements. Make sure to identify the must-haves in the job posting. For example, if it says Ph.D. required and you are just graduating from your undergraduate program, you should not waste your time. On the other hand, if it says “experience with Python, SQL, and AWS” and you know Python and SQL, but you’ve never worked with AWS, you can still apply as you can learn AWS on the job.
Learn to distinguish between job families
A data analyst is not the same thing as a data scientist. Learn the difference between data analytics, data science, and ML engineering disciplines. This will help you optimize your job search and not waste time. You can use the networking informational interviews to learn more about different types of data-related jobs.
6. Make sure you know why you applied for a job
Even if the answer is “Because I need a job and I’m desperate, so I applied to all job postings I came across,” you have to be able to provide an intelligent answer as to why you’re interested and show that you have researched the company. Check out my guide on why you’re not successful in your data science job search for more tips.
7. If you can – be selective
New grads often think that any job in data science is “really cool and awesome”. As you gain experience, you understand nuances such as: Does the company leadership care about what the science department communicates? Is there good data infrastructure at the company, or will you have to spend 95% of your time on data engineering? Is your manager technically capable? Ideally, you want to be able to stay for a while and learn on your first job. You typically don’t want to be the person that knows most about data analysis at the company. Look for places where there are more senior data scientists you can learn from.
Key things to keep in mind to get your first data science job
As a new grad or someone who already has work experience and is looking for a career change but has no formal education in data science, you may have to be a bit more creative than someone with a long list of data science roles on their resume. See this as an opportunity to take your communication and sales skills to the next level.
How to go above and beyond
If you can – do an internship
Apply for internships: they can be a great way to gain real-world experience in data science, learn high-tech tools, and build your network. Look for internships in data science, machine learning, or related fields to get started. The entry bar for an internship is typically lower than for full-time roles. For some internships, knowing the right programming language and showing that you have a good level of critical thinking can be enough.
Stay up-to-date with industry trends
This is a common piece of advice but probably the least useful one, given that time is limited and could be spent otherwise. Let’s be real; no one will hire a junior data scientist to inform them about the latest industry trends. You hire a junior data scientist to do well-defined tasks where you can guide them through the right solution if necessary. This ensures that what they produce is valuable and gives them enough structure to keep learning while delivering solutions. There is nothing wrong with staying informed about the latest developments in data science by reading industry publications and following relevant blogs and podcasts. However, doing too much of this can create the illusion of progress where there is none. At the end of the day, what matters is that you can write code and answer questions during an interview. If you read a blog post about a certain method, instead of reading another 15 blog posts, write some code that implements said method to understand how it works in practice.
Data science is a broad field of study that combines economics, computer science, mathematics, and statistics to uncover insights from data. By combining technical skills with business acumen, data scientists have become some of the most sought-after professionals in today’s tech industry. With their unique blend of analytical thinking and problem-solving abilities, data scientists can create tangible value by extracting meaningful information from raw data sources. Given the broad data science applications, you can find a job in a niche that aligns with your interests. Many data science jobs are remote, which allows for a flexible lifestyle. All of this makes data science a great career to pursue.