Natera is seeking a Manager, Bioinformatics to lead production support for the bioinformatics and data science algorithms behind our Women's Health and Organ Health products. You will lead a team of Data and Bioinformatics Scientists who are accountable for how those algorithms behave on real samples, and you will own the interface between that team and the production organization.

The key challenge of this role is resolving inconclusive reports, where a scientist has to weigh the full body of evidence to reach a determination. You will determine whether an inconclusive report reflects technical constraints or a data quality issue, and how the upstream processes should change so that fewer cases arrive inconclusive. You will maintain a catalogue of known patterns so that new cases can be matched to settled ones, and use AI to assemble and help interpret the evidence at triage.

The job is more than operational, and it is the most cross-functional seat on the team. You’ll need to coordinate across multiple groups – Production Engineering, Product, the laboratory, and R&D – often getting them to align on changes in direction and scope, so success requires leveraging strong communication, negotiation, and consensus-building skills rather than authority.

There is no stated service commitment for this function yet, and defining one is part of the job. You will work out what the team can commit to on turnaround and coverage, get the groups that depend on it to agree, and build the process and staffing that hold it.

Primary Responsibilities:

Support Ownership: Own the support function for our data science and bioinformatics algorithms in production, and make sure its answers are right and arrive when they are still useful.

Investigation and Root Cause: Lead complex investigations that cross a system boundary. Distinguish a biological signal problem from an algorithm problem, a data problem, and an infrastructure problem, and know which group owns the fix. Turn one-off investigations into durable rules and design changes, so the same question does not arrive twice.

Change Validation: Own process validation for pipelines and algorithms as operations change. Define what evidence is required before a change goes into a workflow that is running samples, and hold that bar when there is schedule pressure against it.

Team Leadership and Development: Lead, hire, and grow a team of Data and Bioinformatics Scientists. Lead by example, showing your team how to build and exercise deep systems judgment.

Cross-functional Partnership: Partner with Production Engineering, Product, Bio Development, laboratory operations, and the Data Science R&D teams to resolve operational issues and to prevent the recurring ones. Represent what the algorithms actually do, to groups that depend on them, and negotiate the commitments and the upstream fixes that need their agreement, not yours.

Ways of Working: AI is a routine part of the work here. Support is one of the places where these tools help most and where an unreviewed conclusion does the most damage, so you set the standard: what an agent may conclude on its own, and what has to be reproduced by hand before it goes in a record. We do not screen for prior experience with these tools, and many strong candidates come from environments where they were restricted; we provide the tooling and the ramp time.

What success looks like after a year:

• The recurring escalations are fewer and test TAT reduced, and you can name which recurring issues you eliminated and how, including the upstream fixes and feature requests that made them stop arriving.
• A service commitment exists for the function, agreed with the groups that depend on it, and the team is meeting it.
• Change validation is a defined process the team follows, not a judgment someone makes fresh each time.
• Your scientists handle investigations independently that used to come to you.
• The production organization comes to your team for the answer, not for the route to whoever has it, and the team can settle an inconclusive report without handing it to another group.
• Agent-assisted investigation is normal on the team, with a standard for it that you set.

Qualifications:

• M.S. or Ph.D. in a technical discipline such as Bioinformatics, Computational Biology, Data Science, or a related field. Equivalent depth built through work counts fully.
• 5+ years working with genomic or sequencing data, including 2+ years leading people. We count relevant experience from the point your work became substantially independent, however you got there.
• Demonstrated ownership of a support, escalation, or on-call function with commitments attached, in a setting where being wrong had consequences.
• Demonstrated experience leading an investigation to resolution across more than one system boundary, including deciding when to stop.
• Demonstrated ability to reach agreement across organizational lines without authority: a standard another group adopted, a competing set of priorities brought to a decision, or a position you moved by making the case for it.

Knowledge, Skills, and Abilities:

What we are screening for

• Enough sequencing and bioinformatics fluency to tell a real pipeline failure from an expected one, and to push back when a result looks plausible but is not.
• The ability to reconstruct what happened from logs, intermediate files, and pipeline outputs, working directly in the data yourself.
• Python and SQL used for investigation: pulling records, comparing runs, and building the small tools that keep an answer from having to be derived twice.
• Experience working inside change control, including validation and documentation practice that holds up to review after the fact.
• Experience defining a support process rather than inheriting one: setting a commitment, getting the groups that depend on it to agree, and staffing to hold it.
• People leadership that shows up in specifics: someone whose scope grew under you, someone you hired who worked out, someone you managed out of the wrong job into a better one.
• Enough understanding of the laboratory side to recognize when a data problem started upstream of anything you own.
• Cross-functional relationships as part of the work: enough standing credit with engineering, product, and the laboratory that a technical or operational escalation starts as a conversation instead of a ticket.
• Communication, negotiation, and persuasion at a senior level: the same finding explained to an engineer, to a laboratory director, and to an executive, and a group under pressure given a decision instead of an analysis.

Strong candidates may also have

• Machine learning or statistical analysis experience applied to genomic data. This is genuinely useful here and it is not a gate.
• Experience with cell-free DNA, prenatal, or carrier screening applications.
• Experience in an accredited or high-complexity laboratory setting, or with the practices and standards that apply to a regulated diagnostic.
• Familiarity with workflow systems such as WDL, Nextflow, Snakemake, or Cromwell.
• Experience building observability or alerting that caught a problem before a customer did.