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Data Scientist Job Search

[Webinars: Data Science Job Search & Panel Discussions]

Data Science Job Search and Interview Tips

Job search is a two-stage process.

  • First, you need to make sure that you have the right skills and experience that can get you interviews
  • Second, you need to have the right interview and negotiation skills in order to pass them and land job offers.

If learning data science is already a daunting task, working on your interview skills is not less easy for sure.

It takes good preparation and job search can be a lonely journey. There are times that you don’t hear back from hiring companies at all, and times that you get rejected by hiring managers. So you need to be prepared mentally. The real journey starts when you graduate from school, a bootcamp, or get your data science certification.

The good news is that some of those failed experience will make you tougher and prepare you for the real world.

In general, you need a few things in place before you start your job search:

  • A well-structured resume
  • Coding interview preparation
  • Company research and job applications
  • Mock interview preparation
  • Real interviews and trial and error till you make it

Resume

There’s a plethora of resume how-to information one can find online. Here’re a few things you should note:

  • Keywords matter
  • Highlight your end to end project experience that showcases your relevant skills
  • Structure your resume in a way that it highlights both your data and business sense
  • Certifications are helpful

Putting the right keywords on your resume

Keywords matter because they make your resume noticeable. Most resumes are screened by Applicant Tracking Systems (ATS) nowadays and you need to make sure that you hit the right keywords. WeCloudData’s general suggestions are:

  • Tailor your resume and keywords to job descriptions
  • Make sure you actually know what you put in the resume (learn on the fly if you don’t know)
  • Don’t clutter your resume with excessive keywords

Keywords matter in tech resumes

Relevant Skills

Before you start your data science learning journey, you need to do your research and follow a structured curriculum that helps you learn focus on learning the skills that are most relevant to the job market. If you’re not sure what skills you should learn, refer to the previous chapter on Data Science Learning Path

Make sure you do your research on job postings and work hard to build up practical data skills such as machine learning, big data, and cloud

Both data and business sense are important

This part is hard if you haven’t worked as a data scientist. Showing experience of interacting with business stakeholders, taking in business requirements that you can translate into data requirements, and business presentations are very important. That’s why many FANNG companies also like to ask data and product sense related interview questions.

WeCloudData’s suggestions are:

  • Try to do more readings about data science and ML’s business applications
  • Try to work on projects that are related to specific industries and make sure you tie your analytics outcome to business metrics

Customer lifecycle and predictive analytics opportunities

Different Types of Job Applicants

There are several different types of job applicants. Which one describes you best?

  • New graduates
  • Career switchers coming from non-tech background
  • Data/Software professionals wanting to switch to data science

Resume for New Graduates

Most new graduates struggle with lack of experience. If you’ve graduated with a few interns/co-op experience then you’re blessed. Intern and co-op experience is a big plus in hiring managers’ eyes. If you don’t have intern/co-op experience, make sure you highlight your data science project experience, Github repositories, blog posts, as well as online course certifications. All of these efforts will add up and make you a strong candidate for junior roles.

We’d recommend a one-page resume that looks concise for new graduates. Adding keywords related to the data science skills in a different section will help too.

Resume for Career Switchers

If your past/current work experience is not related to data and analytics, make sure you highlight engineering, analytics, project management, and communication related skills in your resume. However, doing the above is not enough and won’t make you a solid resume to be noticed by hiring managers.

Hiring managers and recruiters typically only spend 10-20 seconds on each resume. So we suggest applicants put portfolio project related experience before work experience. It’s all about what information you prioritize for the reader. Otherwise, hiring manager may very likely skip your resume in 2 seconds.

You need something that catches hiring manager’s attention and nothing works better than interesting projects and the right key skills.

Links to your Github is also a must hive because career switchers will usually need to show extra proof that they have the right skills and experience.

We recommend a one-page resume that prioritizes your key skills and project experience for career switchers. Also highlight your other transferrable skills in past experience.

Resume for Data/Software Professionals

Having data analytics or software background will always give you an edge. That’s good news to data professionals and software engineers.

  • As a data analyst, lots of skills are transferable to data science.
  • As a software developer, strong coding background will be a strong plus to any data science and engineering roles.

However, confirmation bias may exist in interviews.

For example, we’ve noticed that it’s not all that easy for a data analyst to switch to data science unless strong ML experience can be demonstrated. It’s partly because hiring mangers tend to think your data analytics experience is not that advanced and therefore you lack the necessary ML skills even though you have good data wrangling and visualization skills.

If you have strong data analytics experience, make sure you highlight that in your resume. But you probably shouldn’t make your resume feel like that you only specialize in data visualization. So, our suggestion is that you need to remove some experience and add more machine learning and big data related project experience.

We would suggest a 2-page resume for data professionals who want to switch to data science. List enough analytics skills and experience while adding several kaggle project or related ML projects will definitely make your resume favoured by hiring managers.

Job Application

There are a few strategies that are commonly used for job application:

  • Applying for as many jobs as possible
  • Being more selective and regularly applying for a few jobs a week
  • Being proactive and networking
  • Working with recruiters

Usually if you have a direct connection that can refer you to the hiring manager it will be most effective. But if you’re a new grad or career switcher your network in the data social circle might still be very tiny so you don’t always have the luxury to have people who can refer you. Keep in mind that people who refer you usually also want to make sure that you’re a good candidate so they don’t damage the credibility.

Proactive networking is also commonly applied as an effective approach. For example, you can

  • Attend meetup and community events to make connections with like-minded data professionals
  • Attend online/offline career fairs
  • Reaching out to data scientists and hiring managers on LinkedIn

Networking takes effort and patience. It’s not something that will pay off right away. Directly asking a manager to refer you or look at your resume usually don’t work very well for beginners so you’ll need to show some tenacities and persistence. Start networking ad early as possible. Don’t wait till you have started the job search.

Working with agencies are also a good approach because recruiters will save you lots of time and you get a better chance of talking to the hiring manager. The down side is that most recruiters want to work with experienced candidates because it’s directly correlated with their KPIs and commissions. Recruiters need to present strong candidates to the hiring team so they get paid.

So how do you know if recruiters will be interested in you? First, we suggest you to build a very solid LinkedIn profile that has as many details as possible. You can’t change your past experience but you can make your profile interesting to recruiters by adding certifications in cloud and data, data science skills, project descriptions, links to GitHub pages, blog post or LinkedIn posts. Second, wait and see if you get approached by recruiters. If not, be more proactive and reach out to recruiters. Keep a table that records who you contacted and calculate the response rate. Keep polishing your profiles and your cold calling pitch and see if the response rate is improving.

Among all the approaches, applying directly via indeed, LinkedIn or companies’ hiring portal is still the most common one. It’s usually considered as a laborious and boring approach. But it’s effective for junior data scientists to be for couple reasons:

  • The job banks contain most of the jobs
  • It allows you to test the effectiveness of your resume and skill sets

As someone who’s trying to get the foot in the door this is still the best approach and we suggest you spend more effort and apply for as many jobs as possible. Try to apply for jobs with different versions of your resumes and test the responses so you know which version if more effective.

We often hear candidates complain that they don’t get interviews. But after a bit digging we notice that many of them only applied to limited amount of jobs like 20, 30 jobs. While we don’t believe one need to apply for jobs blindly, we do believe that only experience data professionals have the luxury to be more selective. Career switchers and new grads need to spend significant amount of effort on applying for more jobs because the market is very competitive and there are tens of thousands of candidates with similar skills and credentials competing for junior roles. So there’s no excuse for not trying your hardest.

WeCloudData’s general recommendations are as follows:

  • Have a mixed approach in job search
  • At the beginning try to apply for as many jobs as possible in a short period to test which resume version is effective
  • Be proactive on LinkedIn and reach out to hiring managers and recruiters
  • Make sure that you actually have something that catch people’s eyes: portfolio, solid ranking in kaggle competitions, strong GitHub projects, etc. All of these are the outcome of your effort during the data science learning phase.

Interview

Passing data science interview is the last stage in your journey to become a Data Scientist and it’s certainly not an easy one. Data science interview process typically look like the following:

  • Phone screening
    • Phone screening is usually carried out by HR or internal recruiters. The purpose is to screen out candidates that are the wrong fit culturally. Unless there’s something obviously wrong you’d expect to pass this stage easily. If you don’t pass phone screening these are a few reasons:
      • You don’t meet some hard requirements such as skill sets, experience requirements
      • You may sound nervous and doesn’t present enough confidence in communication
  • Coding Challenge
    • Coding challenges are getting more popular. All tech firms will have coding challenges. Many traditional businesses such as banks, retail, and telecom also start to carry out coding screening test. It’s one way to help the company make sure that the candidate meet the minimum technical requirements so it’s not a waste of the hiring manager’s time.
    • With some companies coding challenges are done virtually using platforms such as hackerrank. You need to pass the challenge before you get a chance to meet with the hiring manager. With some other companies it might be part of the interview with a hiring manager. So you might talk to the hiring manager for 30 minutes and then go through a coding challenge with a data scientist.
    • Be prepared to do live coding through screen sharing and even pair programming. It may sound scary and unfortunately there’s no get around. We found that tech companies are more likely to do live coding test.
    • Don’t be intimidated by coding test. Depending on the jobs and companies you apply for, we found that about 30% of the time you will get asked to do live coding. Most of the interviews will have coding challenge that you can do at your own pace. To become a good data scientist you need to write good code so all the preparations are necessary and it helps you improve coding as well. If you don’t want to do it, you may rethink whether data science is the right choice for you.
  • Take-home Assignment
    • Hiring managers want to know how good the candidate is and there’s probably no better way than a take-home test. Companies will give you a few days to complete a more complex data problem. For example, you will get a sample dataset and get asked to build a machine learning model and answer some business questions.
    • Often times candidates also get asked to do presentation if they move on to the next stage. This step is critical because hiring manager want to know
      • How well you can explain the models and processes
      • Quiz your storytelling skills and business sense
  • Team Interview
    • This is by far the most important round in your interview because you get to meet with the hiring manager and the team.
    • Before this round, make sure you work on your personal pitch. Keep it short and concise and allude to a few things such as your portfolio and GitHub so you get a chance to talk about them.
    • Make sure you show the positive side and stay humble yet confident. Be prepared to answer questions like why you want to switch to data science and what kind of effort you’ve put in to get to this point.
    • If you have worked on projects make sure you can articulate them really well. You need to nail this part.
    • If you get to talk to a few data scientists on the team it’s also a great way for you to assess whether you are excited about joining the team since interviews are both ways.
    • Questions may get more technical and tense at this round so make sure you review a few machine learning algorithms that you know well.
  • Final Round: talking to the big boss
    • If you make it to the final round, congratulations, you’re one of the top runners. Companies are usually deciding among 2-3 candidates. Executives are usually busy people so they don’t have many time and patience for interviews. Make sure you articulate your strength and why you will be a good fit for the role. Showing interpersonal skills is important and make sure you know what the team is working on and some of the key business metrics. Ultimately you get hired to augment the team’s capability to achieve the team’s goal.

Mentorship

Job search is a lonely journey. You need to have the right expectations and learn to deal with stress. The following situations will be quite common:

  • You applied for many jobs but didn’t hear back at all
  • You couldn’t pass the HR round
  • You keep failing the technical challenges
  • You get many interviews but keep failing the hiring manager round

It could be mentally exhausting and you feel defeated.

Many candidates would give up at this stage feeling they are not going to make it at all. Therefore, having an experienced mentor to guide you through the process can be very helpful. There are a few places where you may need help:

  • When you’re in doubt, a mentor will be your cheerleader and keep pushing you forward
  • When you fail an interview, a mentor can help you replay the scenario and point out areas that you can improve on
  • Mentors can help you with mock interviews and help you prepare the best pitch
  • Mentorship can help you expect interview questions so you’re well prepared for the tough rounds.
  • An experienced mentor will have regular check-ins and make sure you stay on the right track and apply for enough jobs
  • Mentors also have connections and can sometimes refer you to other connections.

How to find a mentor?

  • You can find mentors through meetup events
  • Another way to find mentors is through LinkedIn cold calling
  • If you need standalone mentorship service go check out WeCareer.AI. WeCareer is an Income-Sharing based mentorship service and has a network of experienced mentors that can help you achieve your goal.
  • If you’re looking for a training program that includes career services and mentorship then WeCloudData’s Data Science Bootcamp is like none other. Our immersive program not only provides the real industry project experience but also includes one-on-one mentorship during job search phase. You can find more info here:

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