Data Science, Cheminformatics & AI: Lab-in-the-Loop Hit Finding

Novartis

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: San Diego, CA
Salary: $138,600–$257,400 / yr
Posted: 81 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $206k

This role $198k

$124k most similar roles pay here $273k

This role pays less than 55% of similar roles. Most pay $157,076–$254,750 — the shaded band above. At the midpoint, this role pays about $198k versus about $206k for comparable roles.

Based on 240 similar postings.

Employer

About Novartis

Novartis is a global biopharmaceutical company that researches, develops, manufactures, and markets prescription drugs in areas including oncology, immunology, neuroscience, and cardiology. Industry: Biopharmaceuticals

Novartis currently has 26 open roles on FindRole.

Listed pay typically runs $194,600–$361,400 across 26 roles with salary data.

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At a glance

TL;DR · Data Science, Cheminformatics & AI: Lab-in-the-Loop Hit Finding

Role Posting Log in to save

Data Science, Cheminformatics & AI: Lab-in-the-Loop Hit Finding is a role within the Discovery Sciences team focused on accelerating low-molecular-weight therapeutic discovery. The successful candidate will lead and execute data science strategies for Lab-in-the-Loop workflows, driving wet/dry lab convergence to improve hit finding in early drug discovery projects. Key responsibilities include developing in silico hit finding strategies using multi-modal data like structure, chemogenomics, and gene expression, while building scalable data pipelines for high-throughput assay data. The role requires expertise in cheminformatics, generative AI, agentic workflows, and drug-target interaction modeling. Required technical skills include the Python scientific ecosystem, SQL, Linux-based high-performance computing, and experience with Make-on-Demand chemistry. Candidates must be proficient in machine learning, active learning, and ligand protein docking to solve complex problems in early-stage hit finding for various disease areas.

Skills

Cheminformatics Data Science Machine Learning Artificial Intelligence Python SQL Deep Learning Generative AI Linux High-Performance Computing Pandas R Data Visualization Active Learning Drug-Target Interaction Modeling Free Energy Perturbation Ligand Protein Docking

What you'll do

Lead and execute data science strategies for Lab-in-the-Loop workflows to accelerate low-molecular-weight therapeutic discovery.
Champion best practices for model development, deployment, monitoring, and prediction telemetry within experimental workflows.
Develop in silico hit finding strategies using internal and external compounds from large virtual chemical spaces.
Apply cheminformatics, generative AI, and multi-modal data to drive impact in early drug discovery projects.
Translate cutting-edge methods like agentic workflows and drug-target interaction modeling into tangible project impacts.
Design and implement scalable, robust data pipelines for high-throughput assay data to enable automated hit-finding.

What we're looking for

PhD in cheminformatics or chemistry, or a related degree with demonstrated applicable experience.
4+ years of post-graduate experience applying cheminformatics, data science, and machine learning to hit finding in early drug discovery.
Experience with hit-finding technologies such as high-throughput screening and advanced phenotypic screening.
Proficiency in the Python scientific ecosystem, including agentic coding, databases, SQL, and reproducible research practices.
Experience working in Linux-based high-performance computing or cloud environments.
Experience implementing AI in Lab-in-the-Loop, iterative, or self-driving lab workflows.
Experience with Make-on-Demand and virtual chemical spaces like Enamine REAL for hit finding.
Excellent scientific communication skills to present complex data science concepts to diverse audiences.

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