Lead Data Scientist (Agentic Solutions)

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., * Agentic Reasoning Loops: Design and program multi-step reasoning frameworks using orchestration frameworks like LangChain, LlamaIndex, 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 at scale.
• Domain-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.

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 (LangChain, LlamaIndex, 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 at scale.
• Data & 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., Snowplow) to help agents deduce funnel health and operational anomalies.
• Environment: Proven ability to operate in a fast-paced, high-ambiguity environment-such as a new product launch or startup-stage AI build-with intellectual curiosity, rigorous attention to detail, and a bias for shipping.
• Ability to stand, sit, or walk for 8-10 hours per day.
• Required to communicate using phone and/or e-mail.
• Ability to view, read, and interpret documents.
• Ability to perform all duties in an office environment that may contain ambient noise and temperature fluctuations.