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Senior Data Scientist – GenAI

Toronto, ON

About Us

Liberate is reimagining how the $2.7T insurance industry works. By starting with voice, the most valuable and complex channel in insurance, the company proved that even the hardest problems can be automated. Now expanding into full workflow automation across sales, servicing, and claims, Liberate is building toward a bold vision: reasoning agents capable of managing the entire spectrum of carrier and broker operations. Trusted by leading brokers and carriers and powered by a team with experience at Metromile, Google, Stripe, and other category-defining companies, Liberate is shaping the future of insurance.

About the role

We’re looking for an experienced Data Scientist to help us push the boundaries of LLM-powered conversational AI and automation pipelines for insurance related tasks.

Location: Boston, MA hybrid role, 2 days per week in-office

What You’ll Do

  • Work with data scientists to design, architect, and build Gen-AI conversation pipelines across modalities (chat, voice and sms) and systems based on multi-agent orchestration, RAG, etc.
  • Design and implement scalable evaluation pipelines to assess enterprise-level AI/ML solutions for performance
  • Partner with machine learning engineers to deploy, operate, and optimize scalable solutions
  • Collaborate with product managers to design user journeys, feedback loops, and analyze user telemetry
  • Develop and implement end-to-end AI/ML product experience in the insurance domain  

What We’re Looking For

  • Demonstrated success in building and scaling GenAI and Agentic AI solutions in a professional environment at scale.
  • Strategic thinker and hands-on builder, able to set high-level architectural vision and dive deep into optimization when needed.
  • Thrive in fast-paced, ambiguous environments and enjoy turning complexity into clear action.
  • 5+ years of industry experience with a proven track record of ownership and delivery of ML/AI-based models/solutions in production environments.
  • Ability to execute trustworthy AI/ML practices in collaboration with stakeholders across the enterprise.
  • Strong communication skills, including the ability to share product innovations through publications, presentations, and other mediums.
  • Proficiency in machine learning algorithms and evaluation frameworks
    • Deep learning frameworks
    • Supervised Finetuning of LLMs
    • Preference Optimization for domain adaptation in LLMs

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