The requirements

PhD or M.S. in Bioinformatics, Statistics, Computer Science, Data Science, Cheminformatics, Software Engineering, or a related quantitative field
Strong software engineering background with experience deploying pipelines at scale in HPC or cloud platforms
Proven experience in high-throughput scientific data analysis, preferably with large-scale screening data, DEL selection outputs, or NGS processing pipelines
Demonstrated ability to architect reliable, user-friendly tools simplifying complex analytical workflows for non-expert scientists
Strong proficiency in Python and statistical programming
Expertise in data visualization frameworks (e.g., Spotfire, Tableau, or modern web dashboarding tools)
Understanding of hit-triaging, noise reduction, statistical ranking, and signal verification in large-scale datasets
Capability in cheminformatics, including handling chemical structures, building-block metadata, or toolkits like RDKit/OpenEye
Knowledge of laboratory data systems (LIMS, SQL, relational databases) and integrating computational tools with experimental workflows is a plus
Critical thinker and problem solver with a continuous improvement mindset
Strong communication skills to translate complex quantitative findings for multidisciplinary and non-quantitative stakeholders

What you'll be doing

Advance DELT data analytics by applying statistical methods, predictive models, and advanced analytical approaches on large-scale DEL screening datasets
Develop intuitive, accessible analytical tools and workflows to empower DELT screening scientists and project teams in evaluating selection results and prioritizing compounds
Design, implement, and maintain automated and reproducible data processing and analysis pipelines for transforming complex experimental outputs into trusted insights
Create computational workflows and searchable building-block data resources to support chemical space exploration, reaction prediction, library design, and synthesis strategy optimization
Collaborate with experimental scientists, computational chemists, and machine learning teams to translate scientific challenges into scalable computational solutions
Communicate scientific insights and technical developments clearly to scientific and multidisciplinary audiences to support knowledge sharing and adoption
Promote scientific excellence through reproducibility, data integrity, robust engineering practices, and contribute to a safe and compliant research environment

Experience integrating computational tools with experimental workflows
Familiarity with laboratory data systems such as LIMS, SQL, and relational databases