Manager of Data Analysis for Lab Production Support, Extended-Hours Support (ExHS)

Natera•San Carlos, CA

5h•$165,000 - $200,000•Hybrid

About The Position

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 lead a team providing key data analytics services for our lab production operations. As a Manager of Data Analysis for Lab Production Support, you will work in one of the three DSPE Extended-Hours Support (ExHS) shift teams, during the specified rotation hours. 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 ensuring the specific ExHS shift team's timely and accurate data analytics support to production teams, e.g., product management team, lab operations teams, lab director teams, and genetic counselor teams.

Requirements

Master's degree in a related field (e.g., Statistics, Data Science, Computer Science).
Minimum 5 years of working experience in a highly regulated environment, e.g., CLIA and/or FDA compliant settings.
Minimum 5 years of hands-on experience in data analysis and/or statistical modeling in data production, data engineering, data analytics, analytics production support roles.
Minimum 2 years in a team management role.
Knowledge of regulatory requirements, such as HIPAA and FDA guidelines.
Working knowledge of the business processes and requirements of diagnostic laboratories.
Proven track record in managing production escalations, data workflows, and team operations.
Proficient in data analysis and statistical techniques.
Proficient in Python and SQL development.
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).
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.
Good documentation practices and coding style.
Strong verbal and written communication skills, with the ability to present technical findings to both technical and non-technical audiences.
Experience using version control systems such as GitHub or GitLab for managing code and collaboration.
Attention to detail and a commitment to data accuracy.
Ability to work collaboratively in a cross-functional team environment.
Strong integrity.

Nice To Haves

Experience with healthcare data, and clinical terminology is a plus.
Functional understanding of next-gen sequencing workflows.
Working knowledge of Snowflake platform technologies.
Familiarity with machine learning techniques and their practical applications in real-world datasets.

Responsibilities

Lead and mentor the specific ExHS shift team, fostering a culture of accountability, innovation, and collaboration.
Measure, report, and continuously improve team operational effectiveness.
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 data analysis tickets.
Train and coach team members on new technologies, procedures, and guidelines/best practices.
Enforce operating procedures for compliance, quality, and efficiency.
Manage and improve tracking systems for workload, capacity, and ticket SLAs.
Represent the Extended-Hours Support (ExHS) teams in stakeholder meetings.
Proactively manage stakeholder expectations.
Establish regular stakeholder reporting mechanisms.
Translate complex analytical results into actionable recommendations for cross-functional partners.
Effectively and correctly escalate issues encountered to other DSPE teams.
Identify opportunities for automation and process enhancements to streamline operations.
Identify and escalate risks such as data model changes that impact project timelines or ticket TAT.
Collaborate with the overall DSPE team to develop new and improve existing support solutions.