Staff Data Scientist, Headway

• PhD or Master’s degree in Statistics or a related quantitative field
• 7+ years of experience in data science, applied machine learning, or a related field
• Strong proficiency in Python, SQL, and experimentation/causal inference techniques
• Direct experience with ranking, search, or recommendation systems (e.g., relevance modeling, click modeling, LTR methods)
• Comfort with ambiguous, open-ended research questions and the ability to translate findings into actionable insights
• Excellent communication skills — able to explain complex ideas simply and build trust across teams
• Passion for improving access to mental health care and advancing outcomes for patients

Desirable

• Our Ranking team’s mission is to help every patient find the right provider for their needs. We are building the matching system that powers this connection, from search and discovery to ranking and personalization. Our goal is to combine cutting-edge machine learning with a deep understanding of patient and provider experience
• We’re looking for a Staff Data Scientist to advance how we connect patients with the right therapist. You will evaluate the product experience and underlying ML systems that help patients find their best-fit clinician
• Your research will power our understanding of the matching performance and help us prioritize experiments to drive improvements. You will work closely with the engineering teams to turn your findings into concrete changes to the patient experience
• This is an opportunity to bring both scientific rigor and empathy to a problem that truly matters — helping people access effective, personalized mental healthcare
• Analyze and evaluate the performance of our search and ranking systems using offline and online metrics
• Develop frameworks to measure relevance, conversion signals, and long-term patient outcomes
• Conduct exploratory research to identify new search and ranking signals, along with opportunities for personalization
• Partner with ML engineers to translate research findings into production-ready models and features
• Collaborate with product, clinical, and engineering teams to define success metrics and guide experimentation
• Clearly communicate findings, recommendations, and business implications to both technical and non-technical stakeholders