Lead Data Scientist

As a Lead Data Scientist at Simple, you will be responsible for enhancing existing algorithms, conducting in-depth performance analyses, and collaborating on developing new product features. Your role will involve validating optimal system configurations, identifying inefficiencies, and evolving personalization algorithms in collaboration with our ML team. This position places emphasis on using data-driven insights to continually improve our health-focused technology.

To be a qualified Lead Data Scientist at Simple, you should possess strong data analysis skills, a solid understanding of machine learning principles, and experience in designing experiments. Proficiency in Python, SQL, and BI tools is essential. Additionally, experience working within efficiency or growth analytics teams and an ability to break down complex technical processes would be beneficial.

Simple highly values the growth of its team members, particularly the Lead Data Scientist role. We offer competitive salary packages, stock options, and technical equipment tailored for your role. Additionally, we provide 21 days of annual leave and opportunities to learn, including language support for those looking to improve their English skills.

Yes! Simple offers a hybrid work format allowing flexibility. You can work remotely if you're residing outside of Cyprus or choose to work from our office located in Limassol. We believe in supporting our team in finding a work-life balance that suits them best.

As a Lead Data Scientist at Simple, you'll engage in various impactful projects, such as leading the implementation of new algorithms for health personalization. You will manage feature launches, measure their impact on business metrics, and work closely with nutrition scientists and developers to ensure our solutions are effective and user-friendly.

When answering this, highlight specific projects where you successfully improved algorithms. Discuss the methodologies you employed, the results achieved, and how your contributions made a positive impact on the overall system performance.

Focus on your systematic approach—how you gather data, formulate hypotheses, conduct analyses, and validate results. Use real examples to illustrate your process and emphasize the importance of data-driven decision-making.

Discuss your familiarity with designing and executing A/B tests, including what metrics you track and how you interpret results. Share an example of a time you successfully used A/B testing to inform product development.

Your response should convey your ability to set effective priorities. Mention how you evaluate project impact, collaborate with stakeholders, and utilize data to determine where to focus your efforts to align with business goals.

Emphasize the importance of effective communication and teamwork. Share your experience in cross-functional collaborations, and explain how you work to ensure alignment and successful feature launches by gauging input from diverse perspectives.

Discuss your understanding of machine learning techniques, as well as your experience with Uplift Models and Reinforcement Learning. Provide examples of how you have implemented these strategies in previous projects, focusing on outcomes.

Mention the tools you're proficient in, such as Python and SQL, and explain how you've used them to derive insights. Discuss your experience with BI tools and how they add value to your data analysis practices.

Share a specific challenge, focusing on the steps you took to address it. Illustrate your analytical thinking and problem-solving skills by detailing your approach, the tools you used, and the successful outcome.

Talk about the resources you utilize to stay informed, such as following industry leaders, attending webinars, and participating in relevant workshops or conferences. Highlight your commitment to continuous learning in a fast-evolving field.

In your response, outline key performance indicators that contribute to success in this role, such as the impact on user engagement, efficiency improvements in algorithms, or successful collaboration with cross-functional teams. Show that you understand the company's goals and how your contributions align with them.