Mgr, Engineering - Data Science

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 the team or validate a new direction

👥 People & Leadership

• Lead a team of Data Scientists working on the hardest problems in applied GenAI — context, agents, AI evaluations, AI safety & guardrails and feedback systems

• Develop your people: 1:1s, career development, performance reviews, and clear growth paths

• Drive hiring excellence: Source, interview, and close exceptional DS talent across I2 to I4 levels

📊 Process & Operational Excellence

• Champion research rigor: Establish norms for AI SDLC, experiment/prototyping hygiene, design reviews, prompt critiques etc

• Track what matters: Drive DS-specific metrics — evaluation coverage, model/prompt quality trends, experiment velocity, and feedback-loop latency

• Optimize team delivery: Unblock ICs, allocate research vs. productionization effort, and maintain a sustainable pace of iteration

In addition to working with amazing colleagues who exemplify our ‘team over self’ core value, you will also have the opportunity to make a traditional data science org into an AI-native org. You will have an opportunity to make a difference.

WHAT WE’RE LOOKING FOR:

As Engineering Manager, Data Science, you'll lead the AI Data Science India pod. You'll set direction on what DS problems we tackle and why — from context engineering and agent design to eval frameworks and feedback loops. You're accountable for problem framing, research quality, and production impact, not for writing every line of code — but when the team is stuck or a new bet needs a proof point, you're the person who opens a notebook and ships a prototype by the end of week.

This is a hands-on EM role for someone who has made the IC-to-EM jump, misses prototyping just enough to still do it, and wants to build the DS function that defines how an enterprise AI product actually works.

THE TEAM:

The Applied AI Engineering team at Clari + Salesloft builds the intelligence layer — the AI that turns all relevant SIGNALS into actionable insight for GTM teams and have agents to automate those actions most of the time. Within Applied AI, our Data Science pod owns the hardest and most ambiguous problems: how to engineer context for LLMs, how to design and govern MCP tool registries, how to orchestrate multi-agent workflows, and how to measure quality in a world where "correct" isn't binary. We work shoulder-to-shoulder with AI Platform, Product, and Engineering to ship AI that revenue teams actually trust.

THE SKILL SET:

• 8+ years of total experience in Data Science / ML

• 7+ years of hands-on experience on DS/ ML and 1+ years of people management experience

• Minimum of 2 years of experience in shipping production LLM / GenAI systems (Graph RAG, agents, fine-tuning, evals, Guardrails etc)

• Track record of DS thought leadership: framing ambiguous AI problems into measurable bets, killing what doesn't work, doubling down on what does

• Deep fluency in: AI eval design, context engineering, retrieval, agent frameworks, MCP, AI guardrails, prompt optimization, feedback-loop instrumentation

• Strong Python prototyping — can independently build a working agent, retrieval pipeline, or eval harness in days

• AI-native practitioner: treats AI as core infrastructure, not a novelty — deep daily use of coding assistants, automation with LLM APIs, and a "try it with AI first" mindset

• Crisp written communication — design docs, research memos, and postmortems that travel beyond the team

• Nice to have: production multi-agent orchestration, enterprise SaaS / revenue-tech domain, open-source or published work in GenAI