Solutions Engineer, Life Sciences

Job Description:

  • Engage deeply with customers to understand their technical problems, workflows, constraints, and frustrations
  • Design and run hands-on proof-of-concept projects tailored to customer environments, including drug discovery model development, clinical analytics pipelines, regulatory submission workflows, and omics data processing
  • Work with account executives to design architectures addressing life sciences requirements for data governance, reproducibility, and validation, including 21 CFR Part 11 and GxP environments
  • Identify novel use cases within life sciences customers and surface opportunities beyond the initial engagement scope
  • Build and maintain reusable technical environments and assets for future customer engagements
  • Partner with Customer Success and Solutions Architects to prepare customers completing POCs for successful production deployment
  • Build custom prototypes and applications on Domino using AI-assisted coding tools to demonstrate customer value beyond standard demos
  • Help convert successful technical engagements into production deployments

Requirements:

  • A technical background from life sciences or a general technology/solutions environment
  • Experience in pharma, biotech, CRO, genomics, medical device, technology, or software environments doing code-first analytical, scientific, or technical work
  • Working knowledge of at least one life sciences domain: drug discovery, clinical development, computational biology, manufacturing/QC, or regulatory data management
  • Platform or tooling ownership experience; ideally having built, operated, or meaningfully contributed to an internal platform or shared scientific computing environment
  • Experience in a solutions engineering, pre-sales, or customer-facing technical role, or equivalent internal/consulting work diagnosing technical problems and helping others solve them
  • Experience leading or contributing to technical evaluations, pilots, or proof-of-concepts
  • Production or near-production coding experience solving a real scientific or operational problem
  • Proficiency in Python and/or R
  • Hands-on experience with data science and ML tools used in life sciences technical work
  • Familiarity with how models are built, validated, and moved toward production in a regulated context is a plus

Benefits:

  • Equity
  • Company bonus or sales commissions/bonuses
  • 401(k) plan
  • Medical, dental, and vision benefits
  • Wellness stipends
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