An AI-driven protein engineering company is seeking a Data Science Contractor to support experimental programs by delivering clean, reproducible analyses of biological data, directly informing experimental decision-making and generating high-quality datasets for model training (GenAI) for the design of novel synthetic biomolecules.
Requirements
- 1–3 years’ experience analyzing experimental/biological datasets (industry, startup, or academic lab).
- Ability to understand experimental context (read protocols, interpret assay outputs) and partner effectively with experimentalists.
- Advanced skills in Python data stack: pandas, numpy, data viz, Jupyter workflows.
- Solid grasp of EDA and basic statistics (distributions, confidence intervals, hypothesis testing).
Responsibilities
- Ingest, clean, and join large multi-plate experimental datasets (e.g., biochemical assays, plate maps, metadata) using Python (pandas, numpy).
- Implement repeatable analyses via notebooks and scripts.
- Select the right summary/metric for the biology and assay geometry and documents the rationale.
- Implement QA/QC checks (sanity checks, outlier flags).
- Produce decision-grade visualizations and concise exploratory data analysis (EDA) summaries.
- Package and share results with clear documentation.
- Collaborate extensively with experimental scientists - asking questions, reflecting on objectives, and agreeing on success criteria before executing.
Other
- Master’s degree in relevant technical discipline with limited prior industry experience or bachelor’s degree in relevant technical discipline with 2+ years of industry experience.
- Translate technical results to the right abstraction level.
- Deliver analyses that directly inform next-step experiments, assay optimization, or go/no-go decisions.
- Thrive in an early-stage start-up environment where you can leverage your agility and expertise to deliver high-quality results.
- Naturally curious and excel when working collaboratively to solve tough problems.
