Data Scientist Operations

About the Role

As a Operations Data Scientist at Kikoff, you will work closely with cross-functional teams including Customer Operations, Product Operations, Product, Engineering, and Data Engineering to make our operations smarter, faster, and more customer-centric. Operations sits at the front line of our customer experience, and you will turn the massive amount of unstructured and structured data generated there into decisions, workflows, and tools that improve both agent and customer outcomes.

This is a high-ownership role. We are looking for someone with an owner mindset who proactively seeks out opportunities to apply a sharper technical and analytical lens to how Ops runs. You will define your own roadmap as often as you execute against someone else's.

• Ops AI ownership: Own and evolve Ops AI, our AI agent that helps Ops investigate customer issues and pull accurate data. Design and iterate on the agent's harness (SQL library, memory, context, skills, tools, guardrails), continuously improve its accuracy and expand its use cases as new workflows emerge.
• Ops Analytics and Measurement: Use data to shape how Ops runs. Define core Ops metrics, build self-serve dashboards and turn unstructured customer data into insights, design and run experiments to turn ops insights into product changes.
• Cross-Functional Collaboration: Partner with Product Ops, CX Ops, and Operation Agents to translate data into decisions, workflows, and tooling that improve customer and agent outcomes.

Qualifications

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field.
• Expert knowledge of SQL.
• Enthusiasm and comfort using AI tools (eg. LLMs, Claude Code) in analytics.
• Strong verbal and written communication skills.
• A humble collaborative can-do attitude, bias to action, and a hunger to apply a technical lens to messy, ambiguous operational problems.
• Solid understanding of statistical analysis, experimentation design, and common ML techniques.

Nice to Have

• Enthusiasm and comfort with using AI in analytics or for fintech problems.
• Experience with Machine Learning especially in a production environment.