Active in over 60 countries and all 50 US States, we protect the customers of the world’s largest digital companies, including Klarna, Revolut, Stripe, Priceline, Agoda, Booking.com, Turkish Airlines, Tongcheng Travel, eBay, and Uber, with seamless, end-to-end experiences. Cover Genius has protected more than 73M customers globally across 240M policies with USD $3.2BN in gross written sales. Active in over 60 countries and all 50 US States, we protect the customers of the world’s largest digital companies, including Klarna, Revolut, Stripe, Priceline, Agoda, Booking.com, Turkish Airlines, Tongcheng Travel, eBay, and Uber, with seamless, end-to-end experiences. Cover Genius has protected more than 73M customers globally across 240M policies with USD $3.2BN in gross written sales. As part of our team, you’ll help drive our AI-first roadmap, developing hyper-personalization engines, agentic distribution, and automated claims infrastructure, while building the scalable technology powering the fast-growing $70B embedded protection market. Accountable, customer-obsessed, collaborative, driven As a Staff Data Scientist for Generative AI on our Customer Ops Team, you will own the design and delivery of agentic AI systems that take on complex, judgement-heavy work across the business, from automated claims decisions to customer operations and partner-facing/customer-facing products. As the Staff Data Scientist, you will set the architecture and evaluation strategy for our agentic systems, build and ship core components yourself, and raise the AI engineering capability of the teams you work with. We are actively considering candidates at both Senior and Staff levels for this role. Final leveling and compensation will be determined based on overall experience and interview performance,



so we encourage you to apply regardless of which title best fits you today Own the design of agentic AI systems. Decide where models add value and where deterministic code should do the work, define agent boundaries, tools and contracts, and design the controls that determine when a system may act on its own. Build the evaluation foundation. Create ground-truth datasets, labelling rubrics with measured inter-rater agreement, and eval harnesses that gate every prompt and model change, measuring both component-level and end-to-end performance against a defined production bar. Ship production systems hands-on. Build prompts, agents, tool integrations, evaluation pipelines and LLM-as-judge scorers, and lead the error analysis that turns failures into specific, prioritised fixes. Run design reviews, pair on implementation, and establish the patterns teams use to build, test and release AI components, so engineers can independently ship eval-gated changes. Raise AI capability across the organisation. Define and track the metrics that matter for each system, such as automation rate, decision accuracy, unsafe-action rate and cost per task, and report to leadership with a clear account of what is moving them and why. Assess new opportunities for agentic AI across the business, recommend what to build, what to buy and what not to automate, and run rapid, falsifiable proofs of concept to decide. 8+ years in data science or machine learning,



including at least 2 to 3 years building and shipping LLM-based or agentic systems into production, where you owned the outcome after launch and not only the prototype. Deep expertise in LLM evaluation: ground-truth dataset and rubric design, LLM-as-judge methods and how to validate them, statistical reasoning with small samples, and error analysis of multi-step or multi-agent systems. Strong Python engineering, with practical experience of agent and tool-use patterns (e.g. Temporal or similar), and a major cloud AI platform (e.g. AWS Bedrock). A track record of influencing technical direction beyond your own team: shaping architecture, influencing engineering standards, and mentoring engineers who were new to AI/ML. Bachelor's degree in a relevant field. A postgraduate degree and/or record of academic achievement is also desirable. You bring data scientists and engineers along by making them better, through clear reasoning, pairing and patient explanation rather than title. Judgement about when not to use AI. Clarity with non-technical audiences. You ship, measure and iterate, own outcomes end to end, and treat a well-evidenced no-go as a valuable result. Champion Our Customers: We lead with empathy and center the customer, turning complex disruptions into seamless moments of trust. Flexible Work Environment
- Our teams are hybrid. We work from home on a Wednesday and Thursday and attend the office on Monday, Tuesday and Friday with flexibility around start/finish times. The Legal & Privacy Stuff Cover Genius promotes diversity and inclusivity. We don't tolerate discrimination, demeaning treatment of anyone, or harassment due to race, national origin, gender, gender identity, sexual orientation, protected ve

📌 Assistant Data-Scientist (Canada)
🏢 Cover Genius
📍 Canada