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Elucidata is a data-centric AI company transforming life sciences through AI-ready platforms. The Senior Scientific Manager will lead technical delivery of multi-omics and AI-driven solutions for BioPharma and biotech customers using Elucidata's Polly platform, a flagship AI-agentic system that helps research teams wrangle, harmonize, and analyze biomedical data at scale.
You will own end-to-end customer engagement delivery—from scoping and experimental design through analysis, interpretation, and final deliverables—ensuring scientific rigor and reproducibility. You'll set the technical direction for complex engagements, integrating multi-omics data with clinical, literature, and genetic evidence (often via knowledge graphs) to answer target discovery and validation questions. You'll lead a multidisciplinary team of bioinformaticians and data scientists, recruiting, mentoring, and developing their careers while maintaining high technical standards.
As the primary scientific point of contact for your accounts, you'll translate customer pain points into robust solutions on the Polly platform. You'll partner closely with Solution Managers and the GTM team to identify and shape upsell and cross-sell opportunities, contributing to scoping, statements of work, and technical proposals. You'll own resourcing, timelines, risk management, and stakeholder communication across your portfolio of engagements.
You'll stay ahead of emerging methods in target discovery and AI-driven drug discovery, bringing innovations into customer work and feeding learnings back to Product and R&D. You'll represent Elucidata externally through workshops, demos, presentations, publications, and conferences.
Required: PhD with 5+ years or Master's with 7+ years in Bioinformatics, Computational Biology, Genomics, or related quantitative discipline. Proven track record owning customer relationships and leading customer-facing delivery. Experience building and managing interdisciplinary technical teams. Hands-on command of multi-omics NGS analysis (bulk and single-cell transcriptomics, spatial technology, genomics). Ability to integrate multi-omics with clinical/real-world data and literature. Hands-on with genetic support for targets (GWAS, burden testing, Open Targets-style evidence). Experience with LLM-based projects. Strong programming in R and Python. Excellent stakeholder-management and communication skills. Commercial mindset for identifying growth opportunities.
Desirable: Knowledge graphs for target discovery, perturbation biology and modeling, LLM engineering depth, scalable infrastructure (AWS/cloud, Docker, Nextflow/Snakemake), strong publication record.
