Senior Data Science Engineer, Clarify Health Solutions

• You have an enthusiastic, energetic personality and an inquiring, investigative mind
• You enjoy finding patterns to automate or simplify work
• You value working with hardworking, curious, and supportive teammates
• You embrace change as an opportunity to learn
• You believe diversity of thought improves our outcomes
• You have a desire to build technologies that will shape the future
• You take great care in the details
• Demonstrable experience in applied machine learning and writing production-level code in the healthcare industry (e.g., biostatistics, health economics, epidemiology, genomics, etc.)
• Resourcefulness in a variety of machine learning packages in Python, with a keen knowledge of optimal selections concerning predictive performance, computation time, and model interpretability
• Self-sufficiency in researching new features and building resources necessary where they do not already exist
• Practicality and systematic problem-solving skills necessary to find elegant solutions to complex problems
• Thrive in a fast-paced environment and capable of managing multiple projects effectively
• An ownership mindset and desire to be the technical expert in the room
• 7+ years of direct data science experience or 5+ years of direct data science experience with a graduate degree in a technical discipline (e.g., computer science, data science, statistics, bioengineering, biostatistics, etc.)
• Proficiency in Python and SQL
• Experience with Machine Learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)

Desirable

• Clarify is looking for Senior Data Science Engineer to focus on machine learning (ML) engineering and operations (MLOps)
• This role will be developing and deploying machine learning (ML) models on large healthcare datasets; proficient in fostering a project through the entire model development lifecycle, including MLOps
• Designing rigorous experiments that incorporate different modeling techniques, feature engineering, and feature selection to optimize model performance, interpretability, and identify actionable insights
• Drive innovation by staying current with cutting-edge research in machine learning, AI, GenAI, LLMs, and related fields, and translating insights into scalable, impactful solutions
• Collaborating with our product management, engineering, clinical, and customer success to provide a data-driven perspective on business problems
• Translating business problems and sometimes complex statistical approaches into actions, balancing creativity with engineering practicality