Lead Data Scientist – Ai Engineer

William Blair

Chicago, United States
Base: $175,000 - $200,000 usd; bonus/equity: discr...
On-site
Design and build integration layer
Develop ai agents and llm applications
Set engineering standards for team
You will be embedded directly with deal teams and industry groups—understanding their workflows firsthand, building AI-powered tools that deliver measurable outcomes, and taking end-to-end accountability for what you ship

Job Summary

  • You will be embedded directly with deal teams and industry groups—understanding their workflows firsthand, building AI-powered tools that deliver measurable outcomes, and taking end-to-end accountability for what you ship.
  • You will set the engineering standards for the team: architecture decisions, code quality, testing practices, and deployment patterns.
  • You will work across the full stack—from data pipelines and ML models to LLM orchestration and Salesforce integrations—with the options to choose the right tool for each problem and the discipline to build for longevity.

Matching Summary

You will be embedded directly with deal teams and industry groups—understanding their workflows firsthand, building AI-powered tools that deliver measurable outcomes, and taking end-to-end accountability for what you ship.

Salary

Base: $175,000 - $200,000 USD; Bonus/Equity: discretionary annual bonus and/or commission-based incentives; Benefits: medical, dental and vision coverage, employer paid short & long-term disability and life insurance, 401(k), profit sharing, paid time off, Maven family & fertility benefit, parental leave, other voluntary benefits

Skills & Requirements

Must-have

  • design and build integration layer
  • develop AI agents and LLM applications
  • set engineering standards for team
  • build and maintain data pipelines
  • implement security and data governance

Nice-to-have

  • embedded directly with deal teams
  • understand workflows firsthand
  • take end-to-end accountability
  • choose the right tool for each problem
  • build for longevity

Key Requirements

  • 5+ years of software engineering experience
  • Hands-on experience building applications with LLMs
  • Experience deploying and operating multi-agent ecosystems
  • Strong ML fundamentals
  • Rigorous engineering practices
  • Familiarity with capital markets
  • Experience with cloud infrastructure

Work Rights

Not specified

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