Data Scientist

Role overview

Responsibilities

• check_circle Lead end-to-end development of machine learning models, from research, data exploration, and feature engineering to training, validation, deployment, and post-production monitoring.
• check_circle Partner closely with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
• check_circle Research, prototype, and implement cutting-edge ML, deep learning, causal inference, and reinforcement learning techniques to tackle complex healthcare challenges.
• check_circle Contribute to ML infrastructure and tooling to ensure scalability, reliability, reproducibility, and performance across all production workflows.
• check_circle Drive innovation by exploring new approaches in Generative AI, LLM-based systems, and AI-agent architectures to enhance recommendation, engagement, and personalization capabilities.
• check_circle 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.
• check_circle 3+ years of hands-on experience developing, deploying, and maintaining ML models in production environments.
• check_circle Bachelor's degree in Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
• check_circle Strong proficiency in Python, including experience with production-grade code practices, version control, testing, and reproducibility.
• check_circle Deep understanding of statistics, probability, causal inference, and experimental design (e.g., hypothesis testing, A/B testing).
• check_circle Expertise with ML techniques such as supervised and unsupervised learning, neural networks, clustering, regression/classification models, and causal modeling.
• check_circle Experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or LightGBM.
• check_circle 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.
• check_circle Experience or strong interest in Generative AI, LLMs, or AI agent systems.
• check_circle Familiarity with recommendation systems, reinforcement learning, or advanced causal inference.
• check_circle Knowledge of cloud platforms (AWS, GCP, Azure), containerization tools (Docker, Kubernetes), and MLOps pipelines.
• check_circle Experience working with healthcare or clinical datasets, including EMR or claims data.

About the company