Head of Data Science and Automation

About the Role

Alta is seeking a Head of Data Science and Automation to own the data and automation backbone behind our research, development, and scale-up. This is a hands-on, player/coach role at the Director level: you will build and run the systems while hiring, leading, and developing a small team of data and automation engineers.

You will own the data pipelines, laboratory information management (LIMS), Ignition, and the integration of laboratory instruments and process equipment, so that every sample, run, and result across our design-build-test-scale cycle is captured, traceable, and ready for analysis and modeling. Our computational biology team relies on this data to build and refine predictive models, and you will partner closely with R&D, Engineering, and the build, test, and scale teams.

We measure this role by its impact on the business: faster design-build-test-learn cycles, higher throughput, data people can trust, and better decisions that move candidates toward commercialization. You will join a collaborative group of scientists and engineers with diverse skill sets, and bring strong communication, critical thinking, and a rigorous, practical approach to problem solving.

Key Responsibilities

• Build and maintain version-controlled, well-documented data pipelines that bring data from instruments, LIMS, sequencing, and process equipment into a central data platform.
• Establish data models, metadata standards, and ontologies so data is findable, linked, and reusable, enabling genotype-to-phenotype tracking across the full workflow.
• Deliver analysis-ready datasets to the computational biology team and define with them the data their models need.
• Implement data quality monitoring, validation, and provenance tracking so results can be trusted and reproduced.
• Provide statistical analysis, experimental design support, and dashboards that help teams make faster, better decisions.
• Select, configure, administer, and extend Alta’s LIMS/ELN and sample management systems, including barcoding, chain of custody, and sample genealogy from design and construct through testing and production lots.
• Integrate LIMS with instruments, liquid handlers, and downstream analytics to eliminate manual data entry and transcription errors.
• Integrate liquid handlers, plate readers, and analytical instruments into automated, scheduled workflows.
• Integrate chromatography pilot and demonstration systems, bioreactors, fermentation, and other process equipment through SCADA, PLC, OPC UA, and process historians, capturing process data in the central data platform.
• Partner with the build, test, and scale teams to find bottlenecks, automate the highest-value steps first, and measure the gains in throughput, cycle time, and error rates.
• Ensure data and automation systems follow sound data integrity and operational technology (OT) security practices, and can scale toward regulated (GxP) requirements as programs advance.
• Hire, manage, mentor, and develop a small team of data engineers, data scientists, and automation engineers, setting clear goals and holding the team accountable for delivery.
• Balance hands-on contribution with team leadership, and ensure strong documentation, SOPs, and training so scientists can use data and automation systems with confidence.
• Communicate roadmaps, trade-offs, and results to stakeholders across the company, and manage vendors and budgets for data and automation systems.

Required Qualifications

• B.S., M.S., or Ph.D. in data science, computer science, chemical or biological engineering, bioinformatics, or a related field, with 8–10 years of relevant experience, including in biotech, pharma, or industrial bioprocessing.
• Demonstrated people leadership: hiring, managing, and developing a technical team while remaining a hands-on individual contributor.
• Proven record of delivering business outcomes, having built data and automation systems that measurably accelerated R&D or manufacturing (for example, higher throughput, shorter cycle times, or candidates advanced toward commercialization).
• Hands-on experience designing and running production data pipelines (Python and SQL, workflow orchestration, cloud data platforms; AWS preferred).
• Experience implementing, configuring, and administering LIMS/ELN and sample management systems.
• Experience integrating laboratory instruments and process equipment, including SCADA, PLC, OPC UA or Modbus, and process historians.
• Working knowledge of statistics, experimental design, and data quality and integrity practices.
• Strong software engineering practices: version control, testing, and documentation.
• Strong written and verbal communication skills, with the ability to translate between scientists, engineers, and leadership.
• Must be a US person as defined by ITAR/EAR, due to export control requirements

Preferred Qualifications

• Experience working alongside build and test functions. Wet-lab expertise is not required, but you should understand experimental and process constraints and work well with the teams who run them.
• Experience with liquid-handling automation and lab scheduling software.
• Experience with bioprocess or fermentation data and scale-up.
• Experience supporting machine learning teams with training data, feature pipelines, and MLOps.
• Familiarity with GxP data integrity requirements (for example, 21 CFR Part 11 and ALCOA+).
• Experience securing networks for connected lab and process equipment.

Compensation & Benefits 

The starting pay range for this position is $190,000 - $230,000, commensurate with educational background and work experience. 

Benefits include:

• 401(K)
• Medical, Dental, Vision plans (or equivalent) 
• Flexible Time Off
• Paid Parental Leave
• Paid Sick Leave
• Company Holidays