2023 New Data Scientists: Job Search Strategies and Realities

What I've Learned after Spending 1700 Min with Mentees

It has been a few months since I became a mentor on ADPList, and I have enjoyed meeting people from all walks of life. I am grateful to have found this platform, which allows me to connect with individuals from diverse backgrounds around the world.

Most of my mentees are new graduates or people who are trying to break into the data science field. I have also had the opportunity to meet a few mid-level to senior data scientists, but I will discuss my learnings from them in a separate post.

Prior to the pandemic, I had some DS mentees when I was actively running R and Python meetups in Los Angeles. Talking to my new mentees from ADPList has surprised me by revealing how much the job market has changed.

The following are my two cents on how to stand out in today's data science job market. Although I have not interviewed in a while, I believe my interview tips can still be useful. I have seen success from my mentees who have taken my advice, and I hope that this can be helpful to whoever is reading.

FAQs

I am not getting any interviews. What do I do?

First of all, are you applying for enough jobs? The job search has always been a numbers game, especially for data scientists without much experience. One of my mentees applied to over 500 jobs before getting a couple of interviews. 

Secondly, are you exploring job listings on different platforms? LinkedIn is not the only destination for job hunting. I recommend a few lesser-known sites:

1. VCs often list jobs for their portfolio companies, which is a great place to search if you are looking to work for a startup. Being listed on VC websites at least means that the company has received funding. Example: Sequoia Job Listings.

2. Wellfound - Startup jobs

3. If you enjoy a particular product or service, visit their company website and explore their job listings. Being familiar with or passionate about the product can help you stand out during interviews. You can be creative with this approach, like I did by reviewing my credit card transactions and applying to companies that sold me a product (if they were hiring data talent).

What industries to focus on?

• Do not focus solely on Big Tech. While these companies were great destinations before 2023, they have gone through several layoffs and painful reorganizations. If you find a good fit, go ahead and apply. However, be aware that there is a risk that the job vacancies could become unavailable in the middle of the interview process or that the company may plan further layoffs after you are hired.

• Expand your job search to traditional industries such as bio/pharmaceuticals, banking, retail, and manufacturing. The majority of layoffs are happening at tech companies and startups that are trying to cut costs or have realized they have overstaffed during the pandemic. However, there are still plenty of opportunities for non-tech companies in traditional industries.

• Startups are fun and offer great learning opportunities. However, the risk of joining a startup is not a myth. Do not be afraid to fail with a startup if you are just starting out and do not have a lot of financial pressure. However, if you do see obvious red flags after learning more about the company, take the job but use it as a transition job to gain experience. Be discerning about the company. Your first few jobs are crucial in shaping your data science career.

I keep failing interviews

Firstly, you have to be knowledgeable about the concepts mentioned in your resume and the job description. No one can really help you if you don't know how to answer fundamental statistical concepts.

Secondly, be honest during your interview. If you are asked a question that you don't know, it's better to admit that you are not familiar with the subject instead of pretending that you understand the question. Encourage the interviewers to ask questions that are relevant to the position and that you are relatively familiar with.

Thirdly, do not ramble. Connect with your interviewer and check in with them often to make sure they are following what you are saying. The last thing you want is for the interviewer to lose interest in what you have to say.

Do I need to do more projects?

Time is precious during a job search. Value everything you do in terms of opportunity costs. In my opinion, you should enrich your resume in the following hierarchical order:

Paid job > Contributions to Open Source Projects > Unpaid job > Personal Projects > Take another online class.

If you have a very cool project idea, you should totally do it… However, it does not really help much if you do another Kaggle projecting predicting housing prices using regression.

I want to be a Machine Learning Engineer.

The job duties of a Machine Learning Engineer can vary greatly. By Machine Learning Engineer, I mean someone who can handle the end-to-end machine learning model development cycle, as well as data science tasks and framework setups such as experiments and analytics. This is still a relatively rare job title, even after the popularity of MLOps in the past couple of years. Essentially, this position requires knowledge of software engineering, system design, DevOps, and data science/ML. In reality, most Machine Learning Engineers specialize in one or a few areas, but not all of them. They often transitioned from either software engineering or data science roles 2-3 years ago, when the job title of Machine Learning Engineer started to gain traction.

The harsh truth is that most companies not only expect to hire senior talent, but they also do not need as many Machine Learning Engineers as they do data engineers or general software engineers. As a result, the job search for Machine Learning Engineers can be more challenging, but it is still possible to pursue this career path.

Till Next Time!

If you find this post helpful, please subscribe and comment! I will talk about how to position yourself in today’s market & how to leverage your past non data science experience in the job search.