Full-Time Head of Data Science and Machine Learning

Atropos Health integrates cutting-edge AI, machine learning, and validated epidemiological methods into its products and we’re looking for a leader to help accelerate that work. The Head of Data Science and Machine Learning  will be a key spokesperson for these innovations with our customers and the market, while also working internally with the product management, engineering, medical, and business teams. Lastly, as is the case with all technical leadership at Atropos, this is a player-coach role and we expect candidates to be comfortable rolling up their sleeves and writing code. 

• Develop the company’s strategy for data science and AI in collaboration with colleagues across Atropos.

• Deliver state-of-the-art innovations and measurable experience improvements across Atropos’s product lines in clinical research, care delivery, and life sciences solutions.

• Contribute code as a independent contributor (IC) to our analytics platform and develop software for our new products (e.g. ChatRWD) 

• Recruit, develop, and lead a team of AI, ML, and data science experts.

• Set and uphold the highest standards of scientific integrity and an ethical approach to the development and use of AI technologies in healthcare.

• Be an outstanding and collaborative leader and cultivate partnerships with customers and external industry and academic collaborators.

• 10+ years of progressive experience in machine learning, data science, AI, or related fields, with 3+ years of demonstrated leadership responsibilities.

• Graduate degree in Computer Science, Statistics, Data Science, Mathematics, Epidemiology, or a related field.

• Pragmatic leadership skills to drive execution in startup environments and a strong personal bias to action.

• Write and coach team on developing production-level code using technologies like Python, R, Git, Docker, etc.

• Comfort with deploying, and scaling analytical products in a cloud environment (e.g., AWS). 

• Experience working with products deployed in a clinical workflow.

• Relationships with life sciences and/or academic collaborators.

• Track record of high-quality publications and research accomplishments.

• Contributions to open source software