As an aspiring data scientist, you may be wondering what it takes to stand out in a competitive job market. While technical skills such as programming and statistics are fundamental, they are not the only factors that employers consider during the hiring process. There is a range of complementary skills that make your fundamental skills stand out. Here are some insights into approaching your first job, which can guide you along your data science career path.
Taking Ownership and Demonstrating Trustworthiness
The key thing that employers look for during an interview, besides obvious competencies, is someone they can trust to take ownership of a task. If an employer hires someone who needs constant supervision, it defeats the purpose of hiring them. Therefore, you should emphasize your strong sense of responsibility, initiative, and ability to produce reliable and valid work.
Consider your first job as an exchange: you provide valuable, dependable work and gain learning experiences in return. This ability to be trusted to work independently is more valuable than having a wide but superficial knowledge base. Let us say I interview a candidate and ask them a question that they do not know the answer to. If they admit that it is outside their area of expertise but explain how they would go about finding a solution, it shows they understand their limitations, are honest, confident, and know how to tackle new problems. However, if they try to answer based on shallow knowledge from a few articles and cannot provide further details when I ask, it is a major concern and I most likely would not hire them.
Communication and Collaboration
While technical skills are necessary, effective communication, often underrated, is equally essential in data science. You must know how to convey complex concepts in a way that non-technical colleagues can comprehend. When I interview, I look for candidates who can brainstorm and contribute to team discussions. It is not just about having the answers, but also about asking the right questions and working together to create innovative solutions. The individuals who interview you are likely to be your future collaborators, and they will look for candidates who they can envision having productive discussions with. During an interview, do not hesitate to think out loud; showcasing your structured thinking often matters more than just having the “right” answers. The attitude you want to convey is that of someone being genuinely interested in finding the right solution, no matter whether it is your or your colleague’s idea that ultimately ends up being implemented.
Demonstrating Skills: Focus on Action, Not Just Information Consumption
A degree alone does not prove your skills to potential employers, which is why job interviews are conducted. In the field of data science, evidence is crucial. To stand out from other job candidates, it is important to focus on producing well-documented work accompanied by high-quality code. You can showcase your projects on your website and in a GitHub repository. This not only highlights your technical skills, but also your ability to take ownership of tasks, collaborate effectively, and communicate your results.
There are numerous open-source databases available, and I suggest finding data related to a problem that genuinely interests you. Remember, modeling forms only a small portion of the work in data science. It is equally important to demonstrate that you know how to clean messy data and understand the impact data quality has on the actual modeling.
The Role of a Manager in Your First Job
When starting your career, it is ideal to have a manager who is more knowledgeable in your field than you are. This allows you to learn from them and receive guidance in effectively communicating technical information to non-technical individuals. Generally, it is not recommended to be the most knowledgeable person in the room in your first job. A manager who understands your work can support your career growth and advocate for you. Therefore, it is important to carefully consider job offers and to approach the interview process as a two-way evaluation where the employer assesses you, and you evaluate the employer.
Conclusion
To pursue a successful career in data science, mastering technical skills is essential, but it is equally important to develop other qualities. You must also demonstrate your ability to take ownership, work collaboratively, communicate effectively, and provide tangible evidence of your knowledge. Finding a mentor and networking with professionals in the field can be extremely helpful. Reach out to people who are where you would like to be in a few years and learn about their journey. Remember, it is a numbers game: the more cold outreach you do, the more people you will end up talking to.
While it can be tempting to watch numerous videos on data science modeling, prioritizing hands-on problem-solving is crucial. Consuming too much content can give a false sense of progress and lead to procrastination. True growth and development require hard work and dedication.
About the Author
Ida Johnsson is a Ph.D. economist who specializes in developing data-driven business strategies with clear KPIs and building interdisciplinary technology teams and data-driven decision frameworks. She is a “full stack economist” experienced in implementing data engineering solutions and writing production-ready data pipelines.
Ida has experience in both early-stage and mature technology companies. Currently, she is an Associate Director on the Applied Economics Research team at Chewy. Additionally, she serves as a Tech Roundtable Chair at the National Association for Business Economics and an Adjunct Professor at USC. At USC, she teaches various courses in econometrics and big data.
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