Senior Data Scientist - Agentic AI

About this role

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Skills you'll use

What you'll do

• Agent Architecture & 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 & 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 & Evaluation: 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.
• GenAI & Revenue Modeling: 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 & 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 & Culture: Mentor senior data scientists on agent-building discipline — not just prompting — and foster a culture of rapid experimentation paired with production rigor.

Who can apply

• Enablement: Contribute to internal documentation, onboarding, and training on our agent components and patterns, promoting platform adoption across teams.
• 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, modeling, or applied ML) prior to 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 & 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 optimization.

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