> Markdown version of [/jobs/ext/1316263-senior-data-scientist-applied-ai](https://www.wearedevelopers.com/jobs/ext/1316263-senior-data-scientist-applied-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist, Applied AI - **Company:** CO-RIPPLING LLC - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $138,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Information Engineering, Data Systems, Database Queries, Software Debugging, Monitoring of Systems, Python (Programming Language), Machine Learning, Software Product Management, Regression Testing, Next.js, Software Engineering, SQL Databases, TypeScript, Web Application Frameworks, Usage Analysis, Retrieval-Augmented Generation, Large Language Models, Grafana, Multi-Agent Systems, Prompt Engineering, Backend, Fastapi, Core Data, Front End Software Development, Automation Anywhere - **Published:** July 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=2d263a382e1a5a88 ## About the Role * 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., * 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. ## Description 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. ## Related Videos - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [All your telemetry data from any source in one place](https://www.wearedevelopers.com/videos/57-all-your-telemetry-data-from-any-source-in-one-place) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)