Solutions Engineer, Life Sciences
Design and lead technical evaluations, prototypes, and architectures for life sciences customers, translating scientific and operational needs into production-ready solutions. Requires Python or R proficiency, life sciences domain knowledge, and experience with customer-facing technical engagements and proof-of-concepts.
About the job
Responsibilities
- Engage deeply with customers to understand technical problems, workflows, constraints, and frustrations, then recommend suitable solutions.
- Design and run hands-on proof-of-concept projects for life sciences use cases, including drug-discovery model development, clinical analytics pipelines, regulatory submission workflows, and omics data processing.
- Partner with account executives to design architectures addressing data governance, reproducibility, validation, 21 CFR Part 11, and GxP requirements.
- Identify novel use cases and opportunities within life sciences customers.
- Build and maintain reusable technical environments and assets for customer engagements.
- Partner with Customer Success and Solutions Architects to transition successful POCs into production deployments.
- Build custom prototypes and applications on Domino using AI-assisted coding tools and support forward-deployed engineering initiatives.
Requirements
- Technical background in life sciences or technology/solutions environments, with experience building and running real systems for users.
- Working knowledge of at least one life sciences domain, such as drug discovery, clinical development, computational biology, manufacturing/QC, or regulatory data management.
- Experience building, operating, or contributing to an internal platform or shared scientific computing environment.
- Experience in solutions engineering, pre-sales, customer-facing technical work, consulting, or equivalent technical problem-solving.
- Experience leading or contributing to technical evaluations, pilots, or proof-of-concepts.
- Production or near-production coding experience solving scientific or operational problems.
- Proficiency in Python and/or R.
- Hands-on experience with data science and machine learning tools used in life sciences.
- Familiarity with building, validating, and moving models toward production in regulated environments.
Compensation
- Total on-target earnings range: $160,000–$230,000 USD.
- Sales compensation includes target commissions or bonuses and annual base salary.
- Additional benefits may include equity, company bonuses or sales commissions, a 401(k) plan, medical, dental, and vision benefits, and wellness stipends.
Skills
Python, R, Machine Learning, Data Science, Proof Of Concept, Solutions Engineering, Data Governance, 21 Cfr Part 11, Gxp, Clinical Analytics, Omics Data Processing, Model Validation
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