Job Description

About the Role
Staff or Senior Data Scientist, Generative AI
Location:
Sydney, New South Wales, Australia
Work Arrangement:
Hybrid — Work from home Wednesday and Thursday; office attendance Monday, Tuesday, and Friday, with flexible start and finish times
Employment Type:
Full-time
Job Description
The Company
Cover Genius is the global infrastructure for embedded protection. 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 73 million customers globally across 240 million policies, with USD $3.2 billion in gross written sales.
Coming off a stellar year with 40% year-over-year revenue growth and a recent $100 million capital raise, putting our valuation at $1.9 billion, we are accelerating into our next phase of growth. As part of our team, you’ll help drive our AI-first roadmap, developing hyper-personalisation engines, agentic distribution, and automated claims infrastructure while building the scalable technology powering the fast-growing $70 billion embedded protection market.
Our people are:
Accountable, customer-obsessed, collaborative, and driven.
Our people are not:
Passive, defensive, siloed, or hesitant.
About the Role
As a Staff Data Scientist for Generative AI on our Customer Operations team, you will own the design and delivery of agentic artificial intelligence systems that take on complex, judgement-heavy work across the business, from automated claims decisions to customer operations and partner-facing and customer-facing products. You will go where the highest-impact problem is, take it from idea to production, and leave behind systems and teams that can keep improving without you.
To drive success in this role, you will have deep hands-on experience taking large language model (LLM)-based and agentic systems from prototype to production, a rigorous approach to evaluation, and the credibility to shape engineering decisions without formal authority.
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. Regular collaboration with Engineering, Product, Operations, Compliance, and Analytics teams will be key to ensuring that the systems we build are accurate, auditable, and safe to scale.
Note:
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.
Key Responsibilities
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 evaluation harnesses that gate every prompt and model change, measuring both component-level and end-to-end performance against a defined production standard.
Ship production systems hands-on.
Build prompts, agents, tool integrations, evaluation pipelines, and large language model-as-judge scorers, and lead the error analysis that turns failures into specific, prioritised fixes.
Lead engineers through influence.
Run design reviews, pair on implementation, and establish the patterns teams use to build, test, and release AI components so engineers can independently ship evaluation-gated changes.
Raise AI capability across the organisation.
Turn each engagement into reusable guidance, templates, shared tooling, and hands-on sessions that other engineering and product teams can adopt.
Own the outcomes, not just the models.
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.
Shape where we invest next.
Assess new opportunities for agentic AI across the business, recommend what to build, what to buy, and what not to automate, and run fast, falsifiable proofs of concept to decide.
What You Will Bring
8+ years of experience
in data science or machine learning, including at least 2–3 years building and shipping large language model-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 skills,
with practical experience of agent and tool-use patterns, such as Model Context Protocol (MCP) and function calling; workflow orchestration, such as Temporal or similar platforms; and a major cloud AI platform, such as Amazon Web Services (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 and machine learning.
Bachelor's degree
in a relevant field. A postgraduate degree and/or a record of academic achievement is also desirable.
Proficiencies and Attributes
Leadership without authority.
You bring data scientists and engineers along by making them better through clear reasoning, pairing, and patient explanation rather than relying on your title.
Judgement about when not to use AI.
You are as comfortable arguing that a component should be plain code as you are designing an agent.
Range and fast ramp-up.
You can walk into an unfamiliar domain, learn how the work is really done from the people who do it, and turn that into a system design quickly.
Clarity with non-technical audiences.
You can explain to an operator, a compliance lead, or an executive what a system does, how confident we should be in it, and why.
Accountable and delivery-focused.
You ship, measure, and iterate; own outcomes end to end; and treat a well-evidenced decision not to proceed as a valuable result.
Why Cover Genius?
At Cover Genius, we create magic by turning the archaic into the extraordinary. We take one of the world’s oldest and most complex industries and reinvent it with world-class technology. Cover Genius doesn’t just disrupt legacy insurance; we make the impossible feel effortless.
Our operating principles guide our mission:
Make today matter:
We deliver with urgency and excellence. We act decisively, move with intention, and hold a high standard.
Act with accountability:
We own our commitments and take pride in delivering results that move us forward.
Grow together:
We are a collective of curious minds. We learn from wins and setbacks, share knowledge generously, and elevate each other every day.
Inspire each other:
We push each other to think bigger and pull together to go further.
Champion our customers:
We lead with empathy and centre the customer, turning complex disruptions into seamless moments of trust.
Flexible work environment:
Our teams are hybrid. We work from home on Wednesday and Thursday and attend the office on Monday, Tuesday, and Friday, with flexibility around start and finish times.
Don't just take our word for it — hear about our culture straight from our people through the company's Story Time.
Ready to Make an Impact?
If you’re looking for a place where you’ll be challenged, trusted, and empowered, we’d love to meet you.
The Legal and Privacy Information
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 veteran status, disability, age, or any other legally protected status.
By submitting your application, you acknowledge that we may collect, store, and process your personal data for recruitment purposes. To ensure a fair evaluation, we may use AI to assist in sorting applications, but all final decisions are made by our hiring team, and no candidate dispositions are automated.
We will keep your information on file for three years from the date of your application.