Data Scientist, Product - job 1 of 2

As a Data Scientist, Product at Jellyfish, you will primarily focus on collaborating with product managers, designers, and engineers to develop innovative and user-friendly features. You’ll apply various techniques including machine learning, natural language processing, and signal processing to refine algorithms and contribute to our platform's development. Furthermore, you're expected to measure the impact of new implementations and continually validate and improve our data science models.

To excel as a Data Scientist, Product at Jellyfish, applicants should have proven data science experience, solid Python programming skills, and a proficient understanding of SQL. A product-focused mindset and the ability to communicate effectively with both technical and non-technical stakeholders are essential. Additionally, candidates should be comfortable managing ambiguity and have a genuine interest in building and deploying algorithms and technologies.

While direct experience with our technology stack, including Python 3, Django, Postgres, Docker, and AWS, is certainly a plus, it is not strictly required. What’s more important is your ability to adapt and learn. If you have a strong foundation in data science and a willingness to pick up new tools, you’ll certainly find success in this role.

At Jellyfish, you can expect a highly collaborative and supportive work environment. The team is made up of talented individuals who are passionate about innovation and enjoy tackling challenges together. We value creativity, continuous learning, and humor, creating not only a productive atmosphere but one that is also enjoyable to be a part of.

As a Data Scientist, Product at Jellyfish, you will have significant opportunities for growth both professionally and personally. By working directly with a cross-functional team and engaging with end-users, you will expand your skill set in product development and data science. Moreover, you’ll have the opportunity to influence product direction and experience firsthand the impact of your work on the company’s growth.

In responding to this question, highlight specific projects where you utilized machine learning techniques to enhance product features or improve user experiences. Discuss the methodology you employed, any challenges you overcame, and how the results benefited the product or company.

When answering this question, focus on the metrics and validation techniques you employ to measure algorithm performance. Mention methods such as A/B testing, user feedback, and statistical significance to demonstrate how you ensure your algorithms deliver the desired value.

To effectively answer this question, share examples of how you’ve communicated and collaborated with team members from different backgrounds. Discuss how you ensure all voices are heard, facilitate discussions to clarify project goals, and help bridge gaps between technical and non-technical stakeholders.

For this question, narrate a specific instance where you encountered a complex data challenge. Describe the context, your analytical approach, the tools used, and how your solution ultimately brought insights or resolved the issue effectively.

When discussing your prioritization skills, it’s important to mention how you evaluate project impact versus effort and the alignment of tasks with strategic goals. Providing an example of how you’ve successfully balanced multiple projects simultaneously will showcase your organizational skills.

For an effective answer, emphasize your ability to simplify complex ideas using analogies, visuals, and clear language. Additionally, discuss your experience in tailoring presentations depending on the audience and ensuring comprehension by soliciting feedback.

In answering, share your methods for keeping up-to-date, such as attending workshops, participating in online courses, reading journals, or engaging in community discussions. This will illustrate your dedication to continuous learning and professional development.

In your response, discuss specific instances where you've taken data models from development to production. Highlight the process you followed, including any testing, validation, and monitoring you put in place to ensure the model's success and reliability in real-world applications.

Addressing ambiguity is key in data science; explain your proactive approach, such as seeking clarification from stakeholders, running exploratory analyses, or iteratively testing assumptions to better define project goals and deliverables.

Highlight key performance indicators that matter most in product development, such as user engagement, conversion rates, and feedback happiness scores. Describe how you gather this data and leverage it to enhance the user experience continuously.