Updated in 2023
We have helped over 1,000 people land great jobs or transition into new careers. After working with so many people on each step of the process, we realized that there is a way to optimize the job search to make it faster and more efficient. We put together this step-by-step guide to help you understand the ways that you too can expedite your search. Read through to learn how to get a data science job that you love.
Step 1: Stand out with a strong resume, LinkedIn, and portfolio
The first action you need to take is to strengthen your resume and LinkedIn. These are a recruiter’s introduction to you as a candidate, but they are not able to spend a lot of time on each person, so yours need to be perfect. They need to tell your story and argue your case for moving forward in the process.
When people show us their resumes, there are 2 elements to their statements that can almost always be optimized. The first is showing impact rather than just explaining the grunt work that you did. The second is quantifying the results or scale of the work you did.
For example, this is a statement that only shows grunt work:
- Compiled and analyzed data from a company-wide survey.
Instead, this statement can be updated to show the impact and quantify the results:
- Built a predictive model to analyze data from a company-wide survey on food-consumption in the office, which resulted in 75% less food wasted each week.
After your content is strengthened, make sure your resume is formatted in a clean and professional way. Use sans serif fonts (fonts without feet) because those are typically more modern. You can use color to differentiate important words or for your name. But, keep it minimal and only 1 cool color (blue, green, purple).
A designated skills section is also a good addition to the format of your resume. This is where you can include the languages and tools that you use. Take a look at the job description and match the words exactly where you can. This helps both recruiters and applicant tracking systems (ATS) because they are looking for exactly those words.
For even more tips on how to make your data science resume as strong as possible, check out this article. To optimize your LinkedIn profile, make sure you have a professional photo, contact information, and a short bio about your experience and the types of roles you are interested in. You should include all of the experience from your resume. But, you can elaborate more on LinkedIn, since you do not have a space limit.
The final step to make you stand out from the crowd is a data science portfolio. Use this to highlight the real-world experience you have and the impact you have made. You can make your portfolio on GitHub and include a deck to explain each project and the data. Check out our outline of a strong data science portfolio for more information.
Step 2: Search smart and find the best opportunities for you
There are a lot of different types of companies and cultures. We often recommend that candidates think about the environment in which they are most likely to thrive before beginning to search for jobs. Asking questions like these is a good way to figure out what you like.
For example:
- Do you want to work at a large company or a small one?
- Does the idea of “wearing many hats” appeal to you or do you prefer staying in one lane?
- Are you more excited to collaborate with others or work on your own?
The way you respond to these questions can help you decide if you want to work at a well-established company or a new startup. When you get a sense of the types of companies that are best for you, you can narrow down the job boards that you use.
- LinkedIn and Google Jobs have a wide variety of options, mainly large companies.
- If you are looking for positions at startups, try AngelList and VentureLoop.
- GitHub and Kaggle have job boards specifically for data science
We ranked and rated even more data science job boards, too, so you can continue to explore open positions.
Step 3. Go beyond the application
Just sending an application into an online portal is not the end of the road. Most people think that it’s a waiting game, but that is not true.
Before sending the application, find the recruiter, hiring manager, or a senior team member on LinkedIn. Then, find their email address through a free service like Clearbit or LeadFinder. You can also try to guess their email addresses using these likely combinations:
- FirstName@company.com
- First.Last@company.com
- FirstInitialLastName@company.com
You can test your guesses in email address verification services like Hunter and Email Checker. You can also use our guide to learn how to reach out to recruiters, which includes a spreadsheet of contact information for Amazon recruiters, Google recruiters, Uber recruiters, and more.
Once you have their email address, apply for the position through the online portal. Next, send a compelling and concise cold email that lets them know you have just applied for the position and you would love to learn more about the company and the role by jumping on a quick call. Make sure that you give them specific times to choose from so that it is as easy as possible for them to say yes.
Here is an example of a template you can use for this cold email:
Hi [name],
I hope you’re doing well! My name is [your name] and I’m reaching out because I recently applied for the [position] position I saw on [platform] and noticed you are a [role] at [company].
While I am not sure if you are the right person to contact, I wanted to reach out to you specifically because I was interested in the work you are doing, specifically [something from their LinkedIn or something the company is working on]. I am a skilled data scientist with machine learning experience and I believe I would hit the ground running and be a great fit for your team.
I would appreciate the opportunity to learn more about you and the company. Would you be free for a 15-minute call, either at [timeframe 1] or [timeframe 2]? In advance, I have attached my resume for your review. I really appreciate your consideration and look forward to hearing from you.
All the best,
[Your name]
Step 4: Go into your technical interviews with confidence by preparing
As you move through the hiring process, you will see technical challenges and interviews. The best advice we can give is to practice what you will see in these types of sessions. There are a lot of ways you can find these questions online. We compiled 113 data science technical interview questions from real tech companies, which is a good place to start.
Remember to read the question thoroughly before you start working. Ask clarifying questions before diving in, like “Over what time period is the data referencing?” or “How do you define this unit?” As you work, explain what you are doing to give the interviewer a sense of your reasoning. Just make sure that you are actually confident in these comments, so that you are not hurting yourself.
Step 5: Prepare for your behavioral interviews, too
Behavioral interviews are just as important as the technical interviews. The reasoning? They want to see how you would react in certain situations and whether or not you are a good fit for the culture and team.
The first behavioral interview you will have will be your phone screen. This is often short and more casual, but that does not mean you shouldn’t prepare. Read our guide on how to prepare for a phone interview here.
The first step to behavioral interview preparation is research. Here are a few places that you should look at before preparing your responses:
- About page: this is where you can learn about their mission and history
- Culture page: review the company values
- Jobs or Careers page: learn about what they are looking for in an ideal candidate and teammate.
- Products page: what product will you be working on? Make sure you can speak knowledgeably about it and the rest of their products, if they have more than one.
We’ve created a guide showing you how to research a company to prepare for your interviews, which you can review for more tips.
This information should go into the responses you practice for behavioral interview questions from top tech companies. Your responses should be specific and succinct. Don’t talk for too long and don’t trail off at the end – instead, offer to “go into more detail” if the interviewer is interested. Write your responses down and practice them in front of a mirror or a friend so that you feel comfortable.
You know you will be asked to introduce yourself, so prepare your elevator pitch in advance. The structure should look something like this:
- Education: Introduce yourself, your major, and your class or year of graduation. This helps recruiters figure out where you fit in the company structure.
- Experience: Talk about what you have done in previous jobs, internships, classes, or even student organizations and activities. Show that you have been taking your education out into the world.
- Projects (optional): If you don’t have much experience, or if you have very impressive projects, mention 1 or 2.
- Conclusion: Make sure to end strong with a preview of your response to “why this company” by adding how you fit with their mission and future goals.
Check out our guide to writing a strong elevator pitch with a template you can adapt for your own use.
Prepare the questions you will ask at the end of the interview, as well. We’ve compiled the best questions to ask in a data science interview, which you can use as a jumping off point.
Step 6: Negotiate politely and make more money
Negotiation actually starts from the first interaction with the company (application or recruiter call usually), so make sure you do not give any numbers or ranges out at all. Everything you say and do throughout all of your interviews are a part of your negotiation, so make sure you are careful.
After receiving an offer, thank the recruiter and tell them you are excited. Don’t say yes or no! Wait until you get off the phone so that you can take a moment and do some research.
According to Glassdoor, average compensation for data scientists is $117k. Data analysts, which is a position that requires a less extensive background, has an average salary of $67k. On AngelList, the average salary for a data scientist is $101k. This is likely a more well-rounded salary average because it includes startups.
Now that you have some background, you can come up with a game plan for your negotiation. Depending on the type of company, you can figure out what the best areas for negotiation are. For example, big tech companies like Facebook, Google, and Amazon typically give good salaries on the first offer, so you might not be able to do much to change that. Give it a shot, but also be prepared to ask for additional equity, signing bonuses, and other benefits.
With smaller startups, your salary might be lower in the first offer, so you should try to negotiate that unless they explicitly mentioned that they cannot go higher. If that is the case, look at bonuses, equity, and benefits so that you can increase your total compensation. Use this negotiation email template, which we annotated, so that make sure you hit the right points and highlight your value to the company.
Step 7: Relax!
It might take some time, but using these tips and templates can help you expedite your job search and help you as you learn how to get a data science job.
If you are looking for more help, Pathrise is a career accelerator that works with students and professionals 1-on-1 so they can land their dream job in tech. With these tips and guidance, we’ve seen 3x as many responses to applications, interview performance scores that double, and a 10-20% increase in salary.
If you want to work with any of our mentors 1-on-1 to get help with any aspect of your data science job search, join Pathrise.
Apply today.
