Senior Data Scientist, Applied AI

About the role

Rippling’s Go-to-Market Analytics team owns a growing suite of internal AI agents and applications used daily by Sales, RevOps, and Customer Success. We’re looking for a senior applied AI builder to help evolve this stack: improving the reliability, quality, and user experience of existing agents while designing and shipping new AI workflows that automate high-leverage GTM processes, surface better business insights, and help teams move faster.

This is a hands-on, high-ownership role on a small team that blends applied AI product development with core data science, ML, and analytics work. You will work across the full applied AI stack: backend systems, data and context pipelines, agent workflows, internal product experiences, and the evaluation and observability systems that make AI quality measurable.

What You Will Do

• Build, launch, and improve AI agents, workflows, and internal applications used by Rippling’s GTM teams.
• Design new agent workflows involving retrieval, tool use, structured context, multi-step reasoning, and human-in-the-loop review.
• Own full-stack feature development for internal AI products, from Python/FastAPI backend services and APIs to Next.js/TypeScript frontend experiences.
• Create SQL/Python pipelines that assemble trusted business context from GTM, product, account, and activity data.
• Apply core data science and ML techniques, including experimentation, predictive modeling, segmentation, forecasting, and product analytics, to identify opportunities, improve GTM workflows, and power AI product features.
• Build and improve the model and agent evaluation infrastructure used to measure quality, catch regressions, and guide iteration, including offline evals, golden datasets, regression tests, human review workflows, and LLM-as-judge evaluation patterns.
• Analyze production traces, usage patterns, latency, token cost, and quality signals using tools such as LangSmith or similar observability platforms.
• Debug and resolve issues across prompts, retrieval, context assembly, tool calls, integrations, latency, and system performance.
• Partner with RevOps, Sales, Customer Success, and Data Science leaders to turn analytical insights and operational pain points into shipped AI product features.
• Establish practical standards for AI quality, safety, monitoring, evaluation, and iteration across Rippling’s internal AI product suite.

What You Will Need

• 3–6 years of experience across data science, applied ML, software engineering, data engineering, or applied AI, including 2+ years of hands-on data science or applied ML work and 1–2 years building or operating production LLM-powered applications.
• Experience in a data science or applied ML role, including building models, designing analyses or experiments, working with business/product data, and translating findings into product or operational impact.
• Strong Python skills, with experience owning backend services, APIs, or production AI/data systems. Experience with FastAPI or an equivalent backend framework is a plus.
• Hands-on experience building production LLM systems, including prompt design, retrieval-augmented generation, tool/function calling, context management, agent orchestration, evaluation, and runtime quality controls.
• Strong SQL skills for data analysis, debugging, and building reliable data/context pipelines.
• Experience analyzing usage, quality, or performance data and using those insights to improve product or system behavior.
• Comfortable owning end-to-end workstreams in ambiguous, fast-moving environments, from problem framing through production launch and iteration.

Nice to have

• Experience with AI evaluation or observability tools such as LangSmith, Braintrust, Langfuse, Arize, or similar.
• Background in experimentation, product analytics, or GTM analytics.
• Experience with Next.js, TypeScript, or other modern frontend frameworks.
• Experience building internal tools or AI products for Sales, Customer Success, RevOps, Support, or other B2B SaaS teams.
• Strong product judgment and ability to communicate technical tradeoffs to non-technical partners.