SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 140,800 - 217,800 / annual
Lila Sciences is building a scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. The company applies AI to every aspect of the scientific method to accelerate discovery in human health, climate, and sustainability.
You will join the Life Sciences AI Data Science team as a bridge between wet-lab science, software engineering, automation, and AI. Your core responsibility is turning experimental data into actionable scientific insight, translating bench workflows into validated analytical requirements, and building reproducible analysis patterns that scale across life sciences programs.
Key responsibilities include:
- Own requirements discovery across lab, automation, software, and AI teams to ensure alignment
- Translate experimental and operational questions into well-scoped, rigorous analyses
- Develop bioinformatics and data-processing pipelines for raw assay outputs
- Build reusable analysis modules that improve reproducibility and consistency 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 both technical and scientific audiences
You will work closely with scientists, automation experts, software engineers, and ML engineers in a deeply cross-functional, collaborative environment.
Required qualifications:
- Strong background in biological sciences, life sciences data, or bioinformatics
- Strong Python proficiency for scientific data analysis (pandas, numpy, 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 experience includes lab automation platforms, bioinformatics tools, workflow orchestrators (Flyte, Temporal), modern Python developer tools (pydantic, pyright, uv), and AWS fundamentals with containerized workflows (Docker, Kubernetes).
