About Us

Alta is reinventing how the world sources critical minerals — using protein-based chemistry to extract rare earth elements and other essential materials from challenging feedstocks with unmatched selectivity. By dramatically reducing the cost and environmental impact of mineral separation, Alta is making it possible to unlock new sources of supply for 21st-century technologies. As global demand accelerates, securing these materials has become one of the most urgent challenges facing U.S. competitiveness and national security. Backed by top investors and government partners, Alta is growing fast and hiring mission-driven scientists and engineers to help us scale our platform and transform the future of mining.

How we work

Speed is part of our DNA. We’re building first-of-a-kind technology and facilities, which means the path isn’t always mapped. We’re comfortable with ambiguity: making sound, timely decisions guided by clear priorities, adapting quickly as we learn, and keeping a relentless focus on moving our business forward. We commit to fixed milestones on compressed timelines and hold ourselves accountable without compromising on safety or rigor.

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.