Senior Data Scientist - Machine Learning

SleepScore Labs

SleepScore Labs is at the forefront of sleep science, using vast datasets to push the boundaries of health and wellness technology. The Senior Data Scientist role exists to develop machine learning solutions that will enhance sleep health globally, working with a team of experts dedicated to pioneering solutions in digital health and product innovation.

In this role you can expect to have the responsibilities:

• Develop and deploy machine learning models to analyze sleep data.
• Utilize statistical techniques to detect patterns and personalize recommendations.
• Design and optimize models from research stages to production.
• Collaborate with diverse teams to implement data-driven solutions.
• Communicate complex insights to various stakeholders.
• Ensure adherence to scientific rigor during model development.

This role comes with the following benefits:

• Competitive compensation and rewards.
• Comprehensive health and wellness programs.
• Innovative and flexible work environment.
• Supportive and inclusive company culture.

This role requires you to have:

• 5+ years in machine learning, statistical analysis, and data science.
• Proficiency in Python (NumPy, Pandas, Scikit-Learn).
• Expertise in deep learning, supervised and unsupervised learning, and time series modeling.
• Strong knowledge of inferential statistics and experimental design.
• SQL proficiency and experience with different data types.
• Advanced degree in a quantitative field (e.g., Computer Science, Statistics).
• Excellent communication skills for conveying complex insights.

You would benefit from having:

• Experience with health-related data models.
• Background in biostatistics or digital health.
• Model deployment in cloud or edge environments.
• Ability to work in interdisciplinary teams.

SleepScore Labs is an equal opportunity employer dedicated to diversity and inclusion. They offer a collaborative workplace adapting to both virtual and in-person interactions, requiring some on-site work.