We are seeking a talented and mission-driven Data Scientist to join our growing Data organization. As a Data Scientist, you will own complex, high-impact projects end-to-end from research and experimentation through production deployment and monitoring.
You will develop and refine the machine learning models that power our personalization engine, engagement optimization, and clinical insights. Your work will directly shape the way millions of people experience their healthcare and improve their well-being.
Responsibilities
Lead end-to-end development of machine learning models, from research, data exploration, and feature engineering to training, validation, deployment, and post-production monitoring.
Partner closely with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
Research, prototype, and implement cutting-edge ML, deep learning, causal inference, and reinforcement learning techniques to tackle complex healthcare challenges.
Contribute to ML infrastructure and tooling to ensure scalability, reliability, reproducibility, and performance across all production workflows.
Drive innovation by exploring new approaches in Generative AI, LLM-based systems, and AI-agent architectures to enhance recommendation, engagement, and personalization capabilities.
Design, execute, and interpret A/B tests and other experimental methodologies to rigorously measure the impact of new models, product features, and interventions.
Collaborate, mentor, and share best practices to elevate technical excellence across the Data Science discipline.
Requirements: 3+ years of hands-on experience developing, deploying, and maintaining ML models in production environments.
Bachelors degree in Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
Strong proficiency in Python, including experience with production-grade code practices, version control, testing, and reproducibility.
Deep understanding of statistics, probability, causal inference, and experimental design (e.g., hypothesis testing, A/B testing).
Expertise with ML techniques such as supervised and unsupervised learning, neural networks, clustering, regression/classification models, and causal modeling.
Experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or LightGBM.
Strong analytical thinking and the ability to clearly translate data-driven insights into actionable business and product recommendations.
Excellent collaboration skills and the ability to work cross-functionally with engineering, product, clinical, and other stakeholders.
Advantage for Candidates With
Experience or strong interest in Generative AI, LLMs, or AI agent systems.
Familiarity with recommendation systems, reinforcement learning, or advanced causal inference.
Knowledge of cloud platforms (AWS, GCP, Azure), containerization tools (Docker, Kubernetes), and MLOps pipelines.
Experience working with healthcare or clinical datasets, including EMR or claims data.
This position is open to all candidates.