Senior Data Scientist - Marketplace Search (x/f/m) - AI Teams - job 1 of 2

As a Senior Data Scientist at Doctolib, your primary responsibilities include finding innovative technical solutions aligned with product goals, implementing and testing your ideas, deploying your algorithms in production, and continuously measuring and improving your approach to ensure optimal results and user satisfaction.

To qualify for the Senior Data Scientist position at Doctolib, you should have at least 6 years of experience in machine learning, proficiency in machine learning frameworks like scikit-learn and PyTorch, significant experience with learning-to-rank methods, and familiarity with big data frameworks such as Spark.

The interview process for the Senior Data Scientist position at Doctolib involves several stages: beginning with an HR screening, followed by a technical deep dive which includes questions and coding assessments, system design discussions, a behavioral interview, and a final interview with the Data & AI Senior Director.

As a Senior Data Scientist at Doctolib, you’ll enjoy benefits such as free health insurance for you and your family, flexible working arrangements, numerous days of RTT, a wellbeing program, and subsidies for public transport, sports, and home-office setups, all aimed at supporting your work-life balance.

In the Senior Data Scientist role at Doctolib, you will work on projects aimed at enhancing user experiences, such as developing AI models that simplify patient-practitioner interactions and improving long-term care management, ultimately supporting Doctolib's mission of transforming healthcare access.

When discussing a machine learning project you've led, emphasize your role, objectives, the techniques you employed, and the impact of your work. Be specific about the challenges you faced and how you overcame them, showcasing your analytical skills and ability to drive results.

In your response, outline your experience with learning-to-rank methods, detailing specific algorithms and situations where you've applied them. Discuss the outcomes and efficiencies gained in projects like refining search results or improving recommendation systems.

When discussing your approach to deploying models, emphasize your steps from model validation to collaboration with ML ops teams. Share your experiences in monitoring post-deployment performance and iterating based on real-time data and user feedback.

You can convey your model validation techniques, including cross-validation, A/B testing, and performance metrics you utilize to evaluate model accuracy. Make sure to mention your iterative approach to refining models based on feedback and analysis.

Share your insights on which data analysis tools you favor, such as Python libraries (like Pandas or NumPy) and visualization tools (like Matplotlib or Seaborn), explaining how they enhance your ability to extract insights and inform decision-making.

For this question, narrate a specific scenario where you employed data science to streamline a process. Discuss the data you analyzed, the solutions you proposed, and the measurable improvements achieved.

Detail your experience collaborating with cross-functional teams, such as data engineers or healthcare professionals, highlighting how effective communication and understanding of diverse perspectives led to successful project outcomes.

Discuss your familiarity with big data frameworks like Spark, emphasizing specific projects where you leveraged these tools to process and analyze large datasets efficiently, enhancing model training and deployment.

Talk about your approaches to staying current in data science, whether through online courses, webinars, attending conferences, or engaging with data science communities. Show that you are proactive about continuous learning.

Share your passion for healthcare and how it aligns with your desire to leverage data for meaningful impact. Illustrate how your experiences have shaped your commitment to improving healthcare access and outcomes through technology.