Lennar

Lead Data Scientist

Lennar

full-time

Posted on:

Location Type: Office

Location: Miami • Florida, Texas • 🇺🇸 United States

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Job Level

Senior

Tech Stack

MicroservicesPythonSQL

About the role

  • Design, build, and deploy autonomous AI agents using frameworks like Amazon Bedrock and AgentCore
  • 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

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
  • 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.
  • Bonus: experience with RAG pipelines, LLM fine-tuning, RLHF, multi-agent orchestration, feature stores, survival analysis/churn modeling, and attribution modeling
Benefits
  • Health insurance (Medical, Dental, Vision coverage)
  • 401(k) Retirement Plan with $1 for $1 Company Match up to 5%
  • Paid Parental Leave
  • Associate Assistance Plan
  • Education Assistance Program
  • Adoption Assistance up to $30,000
  • Up to three weeks of vacation annually
  • Generous Holiday, Sick Leave, and Personal Day policies
  • New Hire Referral Bonus Program
  • Home Purchase Discounts
  • Everyone’s Included Day

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
PythonSQLmachine visionfeature extractionA/B testingmulti-armed banditsuplift modelingdata pipelinesAI development frameworksNLP techniques
Soft skills
collaborative communicationnavigating ambiguityprototypingstakeholder alignmentadaptabilityproblem-solvingtranslating complex conceptsfeedback gatheringexperiment designincremental solution delivery