Lead Data Scientist (Agentic Solutions

Position: Lead Data Scientist (Agentic Solutions)
About Rivian Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what's possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown.

Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. Role Summary Rivian's AI team sits within the Analytics function of the Customer organization-the team that spans Marketing, Sales, and Market Intelligence-and is building the next generation of intelligent, autonomous systems that connect these functions into a single, reasoning ecosystem.

As a Staff/Lead Data Scientist focused on Agentic Solutions, you will design and operationalize the cognitive architecture that powers Rivian's AI agents-building the reasoning loops, retrieval systems, and evaluation frameworks that allow agents to act on live business data with accuracy and reliability. This is a deeply technical, high-impact role at the frontier of applied AI, working closely with Context Engineering, Data Platform, and the broader Customer org Analytics team to bring agentic intelligence to production.

Responsibilities Agent Orchestration & Cognitive Architecture Agentic Reasoning Loops:
Design and program multi-step reasoning frameworks using orchestration frameworks like Lang Chain, Llama Index, or Haystack. Context Engineering & Advanced RAG:
Architect advanced retrieval structures, experimenting with embedding models, dynamic chunking strategies, and token management to ensure agents receive high-fidelity business context. Proactive System Reasoning:
Build the internal logic and evaluation criteria for 'Watchdog' agents, enabling them to analyze live operational data streams, infer anomalies, and formulate proactive insights. Applied Data Science & Model Optimization Small Language Model (SLM) Strategy:
Evaluate, select, and adapt small language models (e.g., Phi, Mistral, Gemma) for domain-specific agentic tasks where precision, latency, and cost efficiency outweigh raw model scale. Design the decision framework for when to use an SLM vs. a frontier model based on task complexity, context requirements, and inference cost. Token Economics & Efficient Agent Design:
Architect agents and orchestration frameworks around token efficiency as a first-class design constraint-developing strategies for context compression, prompt caching, dynamic context windowing, and call minimization to ensure every inference call is purposeful and cost-effective ain-Specific Model Adaptation:
Execute parameter-efficient fine-tuning (e.g., LoRA/QLoRA) and model distillation on open-source models to embed Rivian's fulfillment jargon, vehicle logistics vocabulary, and internal business rules-prioritizing targeted SLM adaptation over large-scale LLM retraining where feasible. Rigorous Evaluation Frameworks:
Establish statistical, model-driven, and human-in-the-loop testing benchmarks to empirically validate agent reasoning, track accuracy drift, and minimize hallucinations. Behavioral Prompt Engineering:
Continuously iterate on complex system prompting, structured output formats (e.g., enforcing strict JSON/tool schemas), and cognitive guardrails to keep agent behavior deterministic and aligned. Qualifications

Education:

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field;
Master's or PhD preferred.

Experience:

5+ years in Machine Learning, Applied Data Science, or AI Engineering, with a proven track record of designing cognitive frameworks, RAG systems, or agentic workflows for business applications. Orchestration Mastery:
Expert-level capability with modern AI orchestration frameworks (Lang Chain, Llama Index, Haystack) and a deep understanding of LLM APIs, prompt engineering methodologies, and agent memory state management. Small Language Model Expertise:
Hands-on experience working with SLMs (e.g., Phi, Mistral, Gemma) for domain-specific deployment; ability to reason about the trade-offs between model size, latency, cost, and task accuracy.

Experience with parameter-efficient fine-tuning (LoRA/QLoRA) and model distillation. Token Management & Cost-Efficient Architecture:
Deep understanding of token economics; experience designing agent frameworks with context compression, prompt caching, dynamic windowing, and call-minimization strategies to build systems that are both intelligent and economically viable a & Language Stack:
Expert-level Python and highly proficient SQL. Comfortable inside Databricks (Spark, Delta Lake) navigating structured relational schemas, metadata, and vector indices to extract and optimize model context. Analytical Literacy:
Strong statistical foundation with familiarity analyzing user behavioral data or event-streams (e.g.,…