Senior Data Scientist, Applied AI

About RipplingRippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system.Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds.Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes.We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses.About the roleRippling’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 doBuild, 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 need3–6 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 haveExperience 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.Additional InformationRippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email View email address on click.appcast.io highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a defined radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location here.A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.