Senior Data Scientist

At Salesloft, our Staff Data Scientist will be a hands-on builder of production AI agents - not a researcher who experiments with agent frameworks on the side. You will be a key member of our fast-growing, high-performing team in India, owning the components that turn an LLM call into a reliable, autonomous system that can reason, act, and recover in production.

We are seeking a Senior Data Scientist who has already spent real time building agents in production - someone who has hit the hard edges of memory, tool-calling, and reliability at scale, and knows how to design around them. If you're looking for an opportunity to learn more, do more, and become more by working alongside a highly motivated and skilled team and to own the agentic core of an enterprise AI platform, this is the career path for you.

Responsibilities:

• Agent Architecture and Technical Strategy: 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.
• Production Agent Engineering: 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).
• Planning and Multi-Step Reasoning: 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.
• Guardrails, Trust and Evaluation: Own the evaluation framework for agentic behaviour - 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.
• GenAI and Revenue Modelling: 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.
• Multi-Agent and Cross-System Coordination: Design how agents talk to sub-agents and other systems (Agent-to-Agent style communication, tool/skill registries shared across agents), so capability is composed rather than rebuilt per use case.
• Cross-Functional Technical Leadership: Partner with Product and Engineering leadership to translate business objectives into concrete agent designs, and serve as the technical anchor who can explain agent behavior and failure modes to non-technical stakeholders.
• Mentorship and Culture: Mentor senior data scientists on agent-building discipline, not just prompting and foster a culture of rapid experimentation paired with production rigour.
• Enablement: Contribute to internal documentation, onboarding, and training on our agent components and patterns, promoting platform adoption across teams.

Requirements:

• 2-3 years of hands-on experience building and deploying AI agents in production, not experimenting with agent frameworks, building demos, or wrapping a single prompt with a tool call.
• Demonstrated ownership of multiple agent components: the reasoning/execution loop, the harness (tool-calling, retries, timeouts, session/state management), memory (short-term context + long-term/episodic), the skill or tool registry, planning/task-decomposition, guardrails, and evaluation.
• A distinct, verifiable data science or ML background (statistics, modelling, or applied ML) before or alongside the agent work - this is a Staff Data Scientist role, not a pure agent/backend engineering role.
• Experience: 7+ years in Data Science or Machine Learning, of which at least 2-3 years must be hands-on building and operating production AI agents (Staff/Lead experience preferred).
• Agentic AI - Component Depth: Practical, production experience.
• Core ML and Stats: Deep expertise in Classical ML (XGBoost, Causal Inference), Time-Series Forecasting, and Deep Learning fundamentals - you understand the math behind the models, not just how to import libraries.
• Generative AI Foundations: Proficiency with modern agent/LLM frameworks (LangChain, LlamaIndex, DSPy, or equivalent in-house harnesses), vector databases (Pinecone, Elasticsearch), and techniques like fine-tuning (PEFT/LoRA) and RAG optimisation.
• Engineering First: Confident in Python. You are open and skilled at writing production-ready code (modular, tested, typed).
• System Design for Agents: Ability to design end-to-end agentic systems and articulate trade-offs between reasoning depth, latency, cost per agent turn, and reliability - and make architectural decisions that hold up at enterprise scale.
• Education: MS or PhD in Computer Science, Statistics, Physics, Math, or an equivalent quantitative field is preferred.