Manager, Data Science

POSITION SUMMARY:

Natera is a global leader in cell-free DNA testing, advancing diagnostics in oncology, women’s health, and organ health to support better patient care. As a Data Science Manager in R&D Operations, you will lead a team of data scientists to design and implement data solutions to accelerate R&D at Natera. You will take ownership of operational data workflows, establish scalable processes and SOPs, and apply ML and/or AI expertise to enable informed decision-making and drive efficiency. You will work closely with our Data Engineering Manager and Data Analytics Manager to deliver integrated, reliable data solutions across the team.

RESPONSIBILITIES:

• Direct, mentor, and develop a team of data scientists, including hiring, performance management, and career development
• Set the technical direction for the data science team and advance its ML/AI capabilities
• Own the data science roadmap for R&D Operations, define success metrics, and report progress and status to R&D Operations and executive stakeholders
• Translate pain points and opportunities surfaced by data analytics into solutions–process changes, algorithms, or new tools–and drive their implementation to deliver measurable process improvement, championing efficiency gains in turnaround time and cost per activity across R&D Operations
• Own the sample governance process end-to-end, ensuring accuracy, timeliness, and accountability, and manage the associated stakeholder relationships
• Develop and oversee machine learning models, optimization strategies, and AI solutions to forecast demand, optimize resource allocation, and automate operational processes
• Lead the development of new data products, as well as the training and enablement around them to grow visibility, self-service, and data-driven decision-making across R&D Operations
• Design, document, and implement processes for data governance, data integrity, and reproducibility across R&D Operations
• Partner with the Data Engineering and Data Analytics Managers and collaborate cross-functionally across R&D and supporting functions to translate business needs into governed, reliable data solutions
• Write clear, testable requirements (e.g. acceptance criteria, data contracts) and define the path from prototype to production in partnership with Data Engineering

QUALIFICATIONS:

• 7+ years of experience in data science in biotechnology, life sciences, or operations research
• Demonstrated people-management experience, including hiring, mentoring, and developing data scientists through coaching on performance and career growth
• Experience owning a data science roadmap and communicating progress to senior and executive stakeholders
• Working knowledge of a relevant scientific or operational domain–biology or life sciences strongly preferred, operations research a close second–sufficient to partner effectively with R&D Operations stakeholders
• Experience working with laboratory or operational data (e.g., laboratory informatics management system (LIMS), sample management, inventory, purchasing/procurement, or equipment operations)
• Track record of driving process improvement–identifying root causes and delivering solutions through process changes, algorithms, or new tools
• Strong background in building data processes, SOPs, and governance frameworks
• Expertise in Python (including pandas, scikit-learn, TensorFlow/PyTorch) and SQL
• Hands-on expertise in machine learning and/or AI applied to real-world problems, setting technical direction for predictive modeling and optimization solutions
• Experience taking ML models or data products from prototype to production, including monitoring and maintenance
• Experience with modern cloud data warehouse and transformation tooling (e.g. Snowflake, dbt)
• Experience partnering with peer functions such as engineering and analytics to deliver end-to-end solutions, including writing requirements or technical specifications
• Clear, direct communication skills to translate complex data insights into actionable business recommendations
• Strong critical thinking and a drive for learning and problem-solving

Nice to Have

• Advanced degree in biology, molecular biology, biochemistry, bioinformatics, or operations research
• Familiarity with data integrity and audit-readiness practices in a regulated (GxP/GCLP) environment