Lead Data Scientist

Lennar•Waterford, FL

About The Position

As a Lead Data Scientist at Lennar, you will design, build, and deploy advanced models and AI agents that shape how Lennar prices, sells, and personalizes experiences for customers across 40+ divisions. You’ll work end-to-end—from research and experimentation to production deployment and monitoring—delivering measurable business impact in pricing, sales, operations, and customer engagement. You’ll collaborate across teams, navigate ambiguity, and drive innovation in a rapidly evolving AI ecosystem. As a Lead Data Scientist at Lennar, you will play a key role in developing and operationalizing AI-driven solutions that transform how we price, sell, and personalize experiences for customers across 40+ divisions. You will architect, build, and deploy intelligent systems—including advanced machine learning models and autonomous AI agents—that directly influence business strategy and customer interaction at scale. In this role, you’ll work end-to-end through the full AI development lifecycle: problem framing, research and experimentation, model and agent development, evaluation, production deployment, and continuous monitoring. Your work will drive measurable impact across pricing optimization, sales acceleration, operational efficiency, and customer engagement. You’ll partner closely with cross-functional teams, bring clarity to ambiguous problems, and lead innovation as Lennar evolves its AI capabilities in a fast-moving technology landscape.

Requirements

Bachelor’s or Master’s degree in Statistics, Economics, Math, Computer Science, Data Science, Machine Learning, or related field (or equivalent experience).
5+ years of relevant experience (1+ with PhD, 3+ with MS) as a data scientist, ML engineer, or applied AI developer delivering production-ready models and systems.
Strong proficiency in Python and SQL , with experience owning the full data science stack (data pipelines + models + deployment).
Hands-on experience with AI development frameworks (LangChain, Strands, Amazon Bedrock, AgentCore, or equivalent).
Experience with experimentation frameworks (A/B testing, uplift modeling, multi-armed bandits, causal ML).
Exposure to machine vision techniques (CNNs, transfer learning, embeddings) and NLP techniques (embeddings, transformers, prompt engineering).
Understanding AI agent observability (evaluation frameworks like LangFuse, RAGAS, Weights & Biases, custom monitoring).
Experience with system integrations : APIs, A2A protocol, MCP servers, orchestration pipelines.
Comfort working with large-scale, imperfect real-world datasets and making progress despite complexity.
Strong engineering skills: ability to design and maintain production pipelines, microservices, and scalable systems.
Proven ability to navigate ambiguity, rapidly prototype, and move solutions into production .
Collaborative communicator who can align technical solutions with business priorities across diverse stakeholders.

Nice To Haves

experience with RAG pipelines, LLM fine-tuning, RLHF, multi-agent orchestration, feature stores, survival analysis/churn modeling, and attribution modeling

Responsibilities

Design, build, and deploy autonomous AI agents using frameworks like Amazon Bedrock and AgentCore to solve business problems in pricing, sales, operations, and customer interactions.
Apply machine vision and feature extraction on home attributes (photos, plans, finishes) to inform premium pricing and personalization strategies.
Engineer and maintain data pipelines and systems supporting all models and agents, ensuring scalability and reliability.
Integrate agents with enterprise systems and protocols (MCP servers, A2A protocol, internal APIs).
Design and run experiments (A/B tests, multi-armed bandits, uplift models) to measure and optimize model and agent performance.
Ensure observability and reliability of deployed agents, including logging, evaluation, monitoring, and drift detection.
Proactively gather feedback from stakeholders and adapt solutions for adoption and measurable impact.
Translate complex data science and statistical concepts into clear recommendations, stories, and visualizations for executives and non-technical audiences.
Favor incremental, explainable solutions that deliver quick wins and scale over time.
Drive experimentation with new tools and approaches, ensuring robustness, governance, and scalability in production deployments.
Share learnings with the broader team to raise the bar on data science and agentic development across the organization.
Manage timelines and expectations transparently with both the data science team and business stakeholders.