Staff Data Scientist - Agentic AI

Salesloft Salesloft · Enterprise · India · Data Science

Staff Data Scientist focused on building and operating production AI agents for an enterprise revenue platform. Responsibilities include defining agent architecture, engineering core components (reasoning, harness, memory, tool calling), designing planning and multi-step reasoning strategies, implementing guardrails and evaluation frameworks, applying GenAI to revenue models, coordinating multi-agent systems, and providing technical leadership and mentorship. Requires significant hands-on experience building production AI agents.

What you'd actually do

  1. Define the roadmap for our agentic AI stack => the execution loop, harness, memory, and tool/skill layers. And decide when an agent, a classical model, or a hybrid is the right tool for a given revenue problem.
  2. Build and operate the core components of our agents end-to-end: the reasoning/execution loop, the harness that manages tool calls, retries, timeouts and session state, short- and long-term memory, and the skill/tool registry agents draw on (via MCP-style tool calling).
  3. Design task-decomposition and planning strategies (evidence-based planning, plan-execute, multi-hop reasoning, research and many more) so agents can coach sellers, inspect deals, raise Forecast risks, update CRMs autonomously and correctly, and perform next best action to save the opportunity from slipping and many more.
  4. Own the evaluation framework for agentic behavior - offline eval, LLM-as-judge, and online A/B testing plus the guardrails (input/output validation, policy and safety checks) that keep agents reliable at enterprise scale.
  5. Apply rigorous statistical and time-series methods to our core revenue models (Forecasting, Deal Health, Risk Prediction), and connect them into agentic workflows where appropriate.

Skills

Required

  • 8+ years in Data Science or Machine Learning
  • at least 2-3 years must be hands-on building and operating production AI agents
  • Practical, production experience with Agentic AI components
  • Deep expertise in Classical ML (XGBoost, Causal Inference)
  • Time-Series Forecasting
  • Deep Learning fundamentals
  • Proficiency with modern agent/LLM frameworks

Nice to have

  • Staff/Lead experience preferred
  • MCP-style tool calling
  • multi-hop reasoning
  • Agent-to-Agent style communication

What the JD emphasized

  • hands-on builder of production AI agents
  • building and operating production AI agents
  • hit the hard edges of memory, tool-calling, and reliability at scale
  • knows how to design around them

Other signals

  • production AI agents
  • LLM call into a reliable, autonomous system
  • reason, act, and recover in production
  • agentic AI stack
  • tool/skill layers
  • MCP-style tool calling
  • multi-hop reasoning
  • agentic behavior evaluation
  • guardrails
  • enterprise scale
  • agentic workflows
  • Multi-Agent & Cross-System Coordination
  • agent-building discipline
  • production rigor
  • building agents in production
  • reliability at scale
  • agentic core of an enterprise AI platform