Sr Data Scientist (Miami)

Senior Data Scientist

Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for our Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live, and fostering a culture of opportunity and growth for our Associates throughout their career. Lennar has been recognized as a Fortune 500 company and consistently ranked among the top homebuilders in the United States.

A Career that Empowers You to Build Your Future

As a Senior 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-endfrom research and experimentation to production deployment and monitoringdelivering 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.

Your Responsibilities on the Team

Design, build, and deploy pricing recommendation models to optimize sales velocity, revenue, and division-level targets.

Develop sales forecasting and demand prediction models to support pricing and inventory decisions.

Build personalization algorithms for tailored product recommendations and communications across email, text, and digital platforms.

Apply machine vision and feature extraction on home attributes (photos, plans, finishes) to inform premium pricing and personalization strategies.

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.

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.

Your Toolbox

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).

Experience with pricing optimization, revenue management, economic modeling, and price elasticity / demand modeling.

Experience building and deploying large-scale recommender systems (collaborative filtering, embeddings, contextual bandits).

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).

Familiarity with real-time or near-real-time systems (Kafka, Kinesis, Flink, or similar) for scalable personalization.

Understanding of 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.

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

Bonus : background in real estate analytics, revenue management systems, or retail pricing optimization.

Physical & Office / Site Presence Requirements :

This is primarily a sedentary office position which requires the incumbent to have the ability to operate computer equipment, speak, hear, bend, stoop, reach, lift, and move and carry up to 25 lbs. Finger dexterity is necessary.

This description outlines the basic responsibilities and requirements for the position noted. This is not a comprehensive listing of all job duties of the Associates. Duties, responsibilities, and activities may change at any time with or without notice.

Lennar is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws.