Boost Your Data Science Job Hunt During Tech Layoffs, Part I
The job market in the tech industry is incredibly challenging right now.
According to layoffs.fyi, 106,630 people have been laid off in the technology sector alone in 2024. If you're a data scientist seeking a new role, it's crucial to be proactive and strategic in your job search to stand out.
In this post, I'll share 5 actionable steps to increase your chances of securing and passing your next Data Science interviews during these tech layoffs.
1. Prioritize Networking and Leveraging Your Professional Connections
Research indicates that you're 4x more likely to get hired through your network compared to just submitting online applications. Networking not only helps you build valuable relationships but also opens doors to opportunities that might not be publicly advertised.
Did you know that up to 70% of jobs are not posted on public job search sites?
Even publicly posted jobs are often filled through referrals or internal candidates. Reach out to former colleagues, classmates, and industry contacts to let them know you're job hunting. You'll be surprised how far a weak tie can take you in your Job Search journey.
2. Be Proactive with Your Connections – Don't Make Them Do the Work For You
When reaching out to your contacts, don't just ask them to inform you about openings. Here are two versions of a message to illustrate the point:
Message 1: "Hi Mark, I am looking for a data scientist role. Please let me know if anything comes up at your company! Thank you, Maggie."
Message 2: "Hi Mark, it was great connecting with you at the data event last month. I am currently looking for my next data scientist role, and I came across this posting at your company. I am really interested in how the team is leveraging machine learning to drive revenue as this aligns well with my expertise. Can you connect me with the hiring manager for this role to discuss more? Thank you, Maggie."
I hope it doesn't come as a surprise that the second message is more likely to get a response. It reminds the person who you are, shows you're actively engaged in your job search, and provides a clear, easy-to-follow action item.
A bonus tip if you are currently a student, make sure you leverage that sentiment by including it somewhere in your cold message! I was able to secure 7 internships while I was in universities all through cold emails and messages on LinkedIn because people LOVE helping students who are dedicated and hold future potentials!
3. Build a Portfolio Website, no really, Build a Portfolio Website
Ok, I know you have heard of this advice numerous times, but hear me out! If you have little to no relevant experience, a portfolio website can significantly boost your chances of getting noticed. And if you have previous experience in NLP but are looking to secure a job in computer vision, a few computer vision projects can show the hiring manager what you are capable of beyond classroom knowledge rather than telling them what you know.
When I chatted with my manager of my first full time data scientists job in tech, he said one of the main reasons I was given a chance for an interview was because of my portfolio website. I ended up getting hired as the only person with a bachelor's degree on the team.
If this is still not convincing you to take the time to build a portfolio website, think of the task of building a portfolio website as a data science project on its own.
If you use packages like rblogdown in R (which is what I did!) or streamlit in Python, in a sense, you are showcasing both soft skills such as storytelling and hard skills such as building a dynamic dashboard through this task.
In order to make your portfolio website stand out, each project needs to have these 4 components:
- Start each project with a concise summary. Explain the problem you tackled, why it is important, and what you aimed to achieve.
- Detail all the methods and techniques you tried and ended up using. Describe your data collection process, the tools/algorithms you used that didn't work, which leads to the final methods you settled on. This section should reflect your technical competence and analytical thinking.
- Present your findings clearly. Use statistics, graphs, and charts to make your results easily digestable. Highly any key insights or actionable conclusions you derived.
- Include next steps, always! Your analysis never stops at your findings in the real world and there are likely alternative methods you didn't explore due to time or resource constraint. Use this section to show that you are have considered these scenarios.
Last but not least, your portfolio projects should be well integrated into your Resume.
Listing up to 3 projects on your resume is ok if you have no prior relevant experience. Otherwise, your portfolio should be a clickable hyperlink at the top of your resume, next to your LinkedIn profile.
4. Prepare for Technical Rounds Early
Technical interviews often include live coding rounds in SQL (for decision data scientist or data analyst positions) or Python (for algorithm data scientist or ml engineer positions). Live coding rounds are NOT designed to be hard and are entirely crackable with practice, even if you've never coded in that language before.
Start practicing at least a month before your interviews. Use platforms like Leetcode for Python and HackerRank for SQL to prepare thoroughly and help you stay consistent. These questions can be quite intimidating at first, but trust me, you will start to detect patterns with enough practice.
One key thing here while you practice is to simulate interview conditions as closely as possible, which means:
- practice solving these problems under timed conditions,
- practice describing your thought process out loud as if there were an interviewer on the other side, and
- use a whiteboard or paper to write out your solutions occasionally to mimic the experience of whiteboard coding in some interview formats.
5. Don't Get Fixated on Specific Job Titles
Understanding what different roles entail in data science rather than focusing solely on the "data scientist" title might just be what you need to break into the data science industry. Roles like data analyst or business analyst can be great stepping stones to becoming a data scientist, while roles like data engineer or machine learning engineer can be in high demand and have better opportunities in the market right now.
- Data Analyst: a data analyst career can be a great stepping stone into data scientist roles. It is more entry-level friendly and can help sharpen many of the skills required for data scientists position, if that is your end goal – data manipulation, data visualization, communicating technical knowledge to non-technical audience, just to name a few.
- Business Analyst: business analysts help identify business areas that need improvement in order to increase efficiency and streamline processes. Business analysts are less technical than data analysts and data scientists but are typically better at understanding business needs and translating them into technical requirements.
- Data Engineer: data engineers are responsible for designing, maintaining, and optimizing data pipelines and databases. If you are more interested in the backend development of a data project rather than the business aspects, you may want to look into a career as a data engineer.
- Machine Learning Engineer: if your interest lies in building predictive models and deploying them at scale, the role of a machine learning engineer might be a great fit. This position is not necessarily entry level friendly, however you can easily demonstrate skills required in your portfolio projects.
All in all, data science as an industry is always evolving, especially with the introduction of AI. Be flexible and ensure you're not limiting your possibilities by being too rigid with job titles.
Interested to learn more? Here is my YouTube video on the same topic!
By being strategic, proactive, and leveraging your network and skills, you can improve your chances of landing a data science role during these challenging times. If you found this post helpful, I also share daily data science and career content on Instagram and YouTube @maggieindata. Feel free to leave any questions in the comments below and follow for part 2!
