Section 1.1: The Variety in Data Scientist Roles

Not all positions labeled as data scientist require extensive machine learning expertise. Focus on roles that lean more towards data analysis, such as assessing A/B tests and developing dashboards with some modeling involved. These roles often have simpler technical interviews, making them more accessible if you’ve faced challenges in more rigorous data scientist assessments. You’ll still engage in model building, but a portion of your work will involve data analyst responsibilities.

Section 1.2: Expanding Your Horizons: Consider Data Analyst Positions

You might question why you should aim for data analyst roles after investing effort in becoming a data scientist. However, aside from technical abilities, strong communication skills and business knowledge are crucial attributes for data scientists. Transitioning to a data analyst position can enhance these soft skills, especially if they’ve hindered your success in landing a data scientist role. The tasks of a data analyst generally have quicker turnaround times, allowing you to tackle diverse business challenges and improve your business acumen. This contrasts with modeling projects, which can take weeks or months to finalize.

Taking on a data analyst role doesn’t preclude you from building models. Companies lacking a dedicated data science team may offer you the opportunity to develop models as needed. In my experience as a data analyst, I utilized machine learning models when they provided the best solution for business challenges. Choosing to work as a data analyst in a field that interests you will deepen your understanding of the business, making you a more competitive candidate for data scientist roles in the future due to your acquired domain expertise.

Additional Options to Consider

Here are some other paths within the data science realm you might explore. If your training has equipped you with strong coding skills, consider transitioning to a data engineering role, particularly if you enjoy programming. For those with an interest in finance, quantitative analyst positions may align well with your background, as there is considerable overlap between the roles of quantitative analysts and data scientists.

If you have a passion for teaching, think about creating your own courses on platforms like Udemy. While earnings can vary, successful instructors can potentially earn over $1 million, and these courses can serve as a valuable addition to your portfolio when applying for future data science positions. Freelancing as a data scientist can also be lucrative, with rates exceeding $100 per hour. Though landing initial gigs may be challenging, establishing strong client relationships can lead to ongoing opportunities.

If a lack of experience is holding you back, consider participating in Kaggle competitions to both demonstrate your data science skills and potentially earn cash prizes, which can go up to $100,000.

As a data scientist, it’s easy to think that you should limit your job applications to roles requiring model building. However, as we’ve discussed, there are numerous other avenues to explore. While these options may not seem perfect, they might be preferable to remaining in a current position that could be at risk due to budget constraints or failing to find a new role because of insufficient experience. Whatever path you choose, I hope your job search concludes swiftly.