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Salary: CAD 135,000 - 220,000 / annual
Cover Genius is the global infrastructure for embedded protection, serving over 60 countries and all 50 US states. The company protects customers of major digital platforms including Klarna, Revolut, Stripe, Priceline, Agoda, Booking.com, Turkish Airlines, eBay, and Uber. With 73M+ customers protected across 240M policies and $3.2BN in gross written sales, Cover Genius recently raised $100M at a $1.9BN valuation and is growing at 40% YoY.
As Staff Data Scientist for Generative AI on the Customer Ops Team, you will own the design and delivery of agentic AI systems that handle complex, judgment-heavy work across the business—from automated claims decisions to customer operations and partner-facing products. You will take problems from idea to production and build systems and teams capable of continuous improvement without your direct involvement.
Key responsibilities include:
- Designing agentic AI systems: determining where models add value versus deterministic code, defining agent boundaries, tools, contracts, and safety controls
- Building evaluation foundations: creating ground-truth datasets, labeling rubrics with measured inter-rater agreement, and eval harnesses that gate every prompt and model change
- Shipping production systems hands-on: building prompts, agents, tool integrations, evaluation pipelines, and LLM-as-judge scorers; leading error analysis to prioritize fixes
- Leading engineers through influence: running design reviews, pairing on implementation, and establishing patterns for building, testing, and releasing AI components
- Raising AI capability across the organization: turning engagements into reusable guidance, templates, shared tooling, and hands-on sessions
- Owning outcomes: defining and tracking metrics like automation rate, decision accuracy, unsafe-action rate, and cost per task; reporting to leadership on drivers
- Shaping investment: assessing new agentic AI opportunities, recommending build/buy/don't-automate decisions, and running fast proofs of concept
The role is hybrid: work from home Wednesday and Thursday, in-office Monday, Tuesday, and Friday with flexible start/finish times.
Requirements:
- 8+ years in data science or machine learning, including 2–3 years building and shipping LLM-based or agentic systems into production where you owned outcomes post-launch
- Deep expertise in LLM evaluation: ground-truth dataset and rubric design, LLM-as-judge methods and validation, statistical reasoning with small samples, error analysis of multi-step/multi-agent systems
- Strong Python engineering with practical experience in agent and tool-use patterns (e.g., MCP, function calling), workflow orchestration (e.g., Temporal), and a major cloud AI platform (e.g., AWS Bedrock)
- Track record of influencing technical direction beyond your own team: shaping architecture, influencing engineering standards, mentoring engineers new to AI/ML
- Bachelor's degree in a relevant field; postgraduate degree and/or academic achievement desirable
- Leadership without authority: ability to bring data scientists and engineers along through clear reasoning, pairing, and patient explanation
- Judgment about when not to use AI: comfort arguing for plain code over agentic solutions
- Range and fast ramp-up: ability to learn unfamiliar domains quickly and turn insights into system designs
- Clarity with non-technical audiences: ability to explain system behavior, confidence levels, and reasoning to operators, compliance leads, and executives
- Accountable and delivery-focused: shipping, measuring, iterating, owning outcomes end-to-end, and treating well-evidenced no-go decisions as valuable results
