Head of Data Science and Automation

Alta Resource Technologies, Inc.•Boulder, CO

7d•$190,000 - $230,000

About The Position

Alta Resource Technologies is seeking a Head of Data Science and Automation to own the data and automation backbone supporting research, development, and scale-up. This is a hands-on, player/coach role at the Director level, responsible for building and operating systems while hiring, leading, and developing a small team of data and automation engineers. The role owns data pipelines, laboratory information management systems (LIMS), Ignition, and integration of laboratory instruments and process equipment. It ensures that samples, runs, and results across the design-build-test-scale cycle are captured, traceable, and ready for analysis and modeling. The role partners closely with computational biology, R&D, Engineering, and build, test, and scale teams to deliver trusted data and automation that accelerate development cycles, increase throughput, and improve decisions that move candidates toward commercialization. Alta Resource Technologies is a next-generation mining company founded in 2023 that uses synthetic biology to separate critical minerals from conventional and unconventional resources, including e-waste. The company is expanding its team in Boulder, Colorado, to address growing mineral demand and develop technologies for a more sustainable mineral supply chain.

Requirements

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 experience in biotech, pharma, or industrial bioprocessing.
Demonstrated people leadership, including hiring, managing, and developing a technical team while remaining a hands-on individual contributor.
Proven record of delivering business outcomes through data and automation systems that measurably accelerated R&D or manufacturing, such as increasing throughput, shortening cycle times, or advancing candidates toward commercialization.
Hands-on experience designing and running production data pipelines using Python and SQL, workflow orchestration, and cloud data platforms.
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, including version control, testing, and documentation.
Strong written and verbal communication skills and the ability to translate between scientists, engineers, and leadership.
Must be a U.S. person as defined by ITAR/EAR due to export control requirements.

Nice To Haves

Experience working alongside build and test functions; wet-lab expertise is not required, but understanding experimental and process constraints and working effectively with the teams running them is expected.
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, such as 21 CFR Part 11 and ALCOA+.
Experience securing networks for connected laboratory and process equipment.
AWS experience is preferred.

Responsibilities

Build and maintain version-controlled, well-documented data pipelines bringing data from instruments, LIMS, sequencing, and process equipment into a central data platform.
Establish data models, metadata standards, and ontologies that make data findable, linked, and reusable, enabling genotype-to-phenotype tracking across the workflow.
Deliver analysis-ready datasets to the computational biology team and define the data their models need in partnership with that team.
Implement data quality monitoring, validation, and provenance tracking so results are trustworthy and reproducible.
Provide statistical analysis, experimental design support, and dashboards to help teams make faster, better decisions.
Select, configure, administer, and extend 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 build, test, and scale teams to identify bottlenecks, prioritize high-value automation, and measure gains in throughput, cycle time, and error rates.
Ensure data and automation systems follow sound data integrity and operational technology 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; set clear goals and hold 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 confidently.
Communicate roadmaps, trade-offs, and results to stakeholders across the company, and manage vendors and budgets for data and automation systems.