Manager, Data Science Production Engineering at Natera

The Manager of Data Science Production Engineering (DSPE) will oversee a team responsible for ensuring the successful production escalation resolutions, statistical analysis, maintenance of comprehensive product health dashboards, and post-launch support and monitoring services. This role will focus on enhancing the reliability of production support workflows, improving processes, and enabling teams to deliver actionable insights across the Oncology business unit. The Manager will act as a bridge between stakeholders, ensuring that operational needs are met and aligned with organizational goals.

• Lead and mentor the DSPE team, fostering a culture of accountability, innovation, and collaboration.

• Define and track team objectives, ensuring alignment with broader organizational goals.

• Provide career growth opportunities, feedback, and performance assessments for team members.

• Encourage individual contributors (ICs) to pitch ideas for process improvements and innovations.

• Ensure timely and accurate resolution of production escalations and data analysis tickets.

• Oversee the development and maintenance of de-identified data products, ensuring compliance with data privacy standards.

• Drive improvements in the onboarding documentation and code repository for seamless team operations.

• Manage and improve tracking systems for workload, capacity, and ticket SLAs.

• Serve as the primary point of contact for internal stakeholders, ensuring clear communication and satisfaction.

• Address friction caused by last-minute requirements or unclear expectations by implementing proactive communication strategies.

• Collaborate with Lab Directors, R&D, and senior leadership to provide valuable insights through dashboards and reports.

• Identify opportunities for automation and process enhancements to streamline operations.

• Mitigate risks such as data model changes that impact project timelines or ticket TAT.

• Establish scalable standards for hypercare and post-launch monitoring support.

• Collaborate with the team to develop and refine statistical models for predictive analytics

• Contribute to defining clear mandates, roles, and responsibilities for cross-functional teams.

• Align DSPE initiatives with organizational goals by delivering actionable insights and data products.

• Set a new benchmark for product health dashboards and automated root cause analysis.

• Master’s degree in a related field (e.g., Statistics, Data Science, Computer Science).

• Minimum of 5+ years of experience in clinical data analysis or a related role preferably in a healthcare or pharmaceutical setting, with 2+ years in a leadership role.

• Knowledge of clinical research methodologies and regulatory requirements, such as HIPAA and FDA guidelines.

• Proven track record in managing production escalations, data workflows, and team operations.

• Experience with healthcare data, and clinical terminology is a plus.

• Functional understanding of next-gen sequencing workflows.

• Experience working with machine learning methods to clinical and/or genomic data.

• Strong proficiency in data analysis tools and programming languages (e.g., SQL, Python, R).

• Proficiency in statistical analysis tools (e.g., R, SAS) and data visualization tools (e.g., Tableau, Power BI, Quick Sight).

• Strong SQL skills for querying and retrieving data from complex data sources in a structured format.

• Strong understanding of various statistical concepts (parametric & non-parametric distributions, hypothesis testing, probability, regression), and machine learning

• Proficient in aggregating data, preparing, cleaning data, and data mining using SQL and Python.

• Strong leadership and team management skills.

• Excellent problem-solving and critical-thinking abilities.

• Excellent interpersonal and communication skills, with the ability to align diverse teams toward a common goal.

• Attention to detail and a commitment to data accuracy.

• Ability to work collaboratively in a cross-functional team environment.