About the role
Clari + Salesloft are building the next era of enterprise revenue — one where teams make confident decisions powered by AI and real signals. By combining our scale, insights, and AI innovation, we’re building the industry’s first Predictive Revenue System , enabling humans and AI to work together to make smarter decisions and drive consistent growth. With thousands of customers using our platforms every day, we have an unmatched view into how revenue is actually won — the Revenue Context that reveals what happens, when, and with what outcome. This gives us a unique opportunity to transform an entire category and set a new benchmark for how modern revenue teams operate. Join us to help transform how companies around the world run revenue — and build the platform that will guide leading revenue teams into the future. Job Title: Mgr, Engineering - Data Science Location: India, Hybrid This is a hybrid position, which will require the ability to be onsite in our Bengaluru, India office as needed. Candidates must be based in India. THE OPPORTUNITY: At Clari + Salesloft, our Engineering Manager, Data Science will be pivotal to our company’s success. You will be a key member of our fast-growing and high-performing Applied AI Engineering org — setting the what and why for every DS problem we tackle: context engineering, MCP/tool registries, AMA, multi-agent orchestration, deep agents, eval frameworks, prompt optimization, AI guardrails, feedback loops and more. You'll think like a Staff DS, operate like an EM, and ship like a founder — prototyping alongside
the team when it matters and stepping back to coach when it doesn't. You will be working in lockstep with AI Platform, Product, and GTM. On a day-to-day basis, you will be responsible for 🎯 Product & Delivery Own the DS roadmap: Partner with Product and Engineering to translate ambiguous org’s bets into scoped DS problems with measurable hypotheses Drive cross-functional alignment: Work in lockstep with AI Platform, Product, and GTM to ensure DS work lands in production with real user impact Balance research with shipping: Decide when to explore, when to converge, and when to kill a bet — and make those trade-offs legible to leadership ⚙️ Research & Technical Direction Set the technical direction for context engineering, agent frameworks, MCP tool registries, and multi-agent orchestration patterns Own the eval strategy: Define what "good" means for each DS system — offline benchmarks, online metrics, regression gates, and human feedback loops Prototype when it matters: Stay hands-on enough to independently build a working agent, retrieval pipeline, or eval harness to unblock