About the role
Important: this position will work on the Weekend Shift team, with the following rotation schedules:
- Tuesday-to-Saturday (including Tue, Wed, Thur, Fri, and Sat), 9AM-to-5PM.
- Sunday-to-Thursday (including Sun, Mon, Tue, Wed, and Thur), 9AM-to-5PM.
This position will rotate through the above work schedules quarterly, i.e., work in one rotation schedule for 3 calendar months, and then move to the other rotation schedule for another 3 calendar months.
This is an exciting opportunity in our Data Science Production Engineering (DSPE) department at Natera (a global leader in precision medicine and genomics testing), to provide critical data analytics support for our lab production operations. As a Senior Data Analyst for Lab Production Support in our DSPE Extended-Hours Support (ExHS) team, you will work in the Weekend Shift team (following the above two rotation schedules). You will have the opportunity to apply innovative data analytics solutions, directly support the most cutting-edge medical diagnostics and genomics applications, and make real & immediate impact to patients and healthcare providers, by providing timely & accurate data analysis support to Natera's production laboratories, as well as for other key business needs.
PRIMARY RESPONSIBILITIES
Technical Execution and Infrastructure
- Deliver timely data support to Lab Operations teams, unblock production issues, and improve patient results turnaround times.
- Apply statistical techniques to analyze complex datasets, diagnose operational issues, and support data-driven decision-making.
- Perform exploratory data analysis, identify trends, and work with stakeholders to interpret findings and drive continuous improvement.
- Design and implement automated solutions for recurring data analysis and production support workflows, improving operational efficiency and scalability.
- Extract, transform, and analyze data to generate actionable insights that support lab production operations and business decisions.
- Document analytical processes, best practices, and insights to ensure consistency and knowledge transfer across teams.
Process Improvement
- Develop and optimize solutions for efficient data retrieval and scalable performance on large and/or complex datasets.
- Learn and acquire operational efficiencies on new technologies and solutions developed by other DSPE teams.