Data Scientist II / Senior Data Scientist, Life Sciences

Your Impact at LILA

Lila Sciences is building the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method, enabling scientists to solve challenges in human health, climate, and sustainability at a pace and scale not previously possible.

At Lila, teams are deeply cross-functional and collaborative. We seek people with inclusive mindsets, diverse perspectives, and comfort working in creative, fast-moving environments. If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

We are seeking a Data Scientist, Life Sciences to join Life Sciences AI Data Science (LSAI-DS) and own the bridge between wet-lab science, software engineering, automation, and AI. This person will turn experimental data into actionable scientific insight, translate bench workflows into validated analytical requirements, and build reproducible analysis patterns that scale across life sciences programs.

This role is ideal for someone who combines life sciences depth, strong Python-based analysis skills, applied statistics, and user-centered thinking. You will work closely with scientists, automation experts, software engineers, and ML engineers to make sure data flows, analyses, and tools reflect what is actually happening at the bench.

What You'll Be Building

• Own requirements discovery across lab, automation, software, and AI teams.
• Translate experimental and operational questions into well-scoped analyses.
• Develop bioinformatics and data-processing pipelines for raw assay outputs.
• Build reusable analysis modules that improve reproducibility across studies.
• Integrate analytical workflows with centralized data lake and AI platform systems.
• Identify gaps in LIMS flows, data management, and automation workflows.
• Document methods, assumptions, and results for technical and scientific audiences.

What You'll Need to Succeed

• Strong background in biological sciences, life sciences data, or bioinformatics.
• Strong Python proficiency for scientific data analysis, including pandas, numpy, and scipy.
• Solid grounding in applied statistics and exploratory data analysis.
• Experience eliciting, documenting, and validating requirements from scientific and technical stakeholders.
• Ability to identify unstated assumptions and clarify ambiguous analytical requests.
• Experience producing reproducible, well-documented analyses.
• Strong communication skills with both technical collaborators and bench scientists.
• Proactive, detail-oriented approach to ambiguous scientific problems.

Bonus Points For

• Experience working with lab automation platforms or instrument data systems.
• Familiarity with common bioinformatics tools
• Experience with workflow orchestrators such as Flyte or Temporal.
• Knowledge of modern Python developer tools such as pydantic, pyright, or uv.
• Familiarity with AWS fundamentals and containerized workflows, including Docker or Kubernetes.