Sr. Data Scientist - People Analytics

Lennar•Sacramento, CA

2d•$122,713 - $163,618

About The Position

As a Senior Data Scientist on Lennar's People Analytics team, you will develop models to forecast workforce outcomes, including attrition and turnover risk, hiring and headcount needs, and related trends. You will also build AI agents that deliver analytical work to HR leaders so forecasts can inform decisions. This is a newly created position and the first dedicated data science role on the People Analytics team, with significant latitude to shape modeling approaches, validation standards, and how results are presented to senior leadership. You will work closely with HR Centers of Excellence and use Workday and other people systems, as well as metrics surfaced through Visier. The role reports to the Head of People Analytics. The position involves building predictive models and AI agents, working with structured and unstructured workforce data, partnering with Data Engineering on data pipelines, and communicating findings to technical and non-technical audiences. It also requires discretion in handling sensitive workforce and compensation data. This is primarily a sedentary office position. The stated base compensation range is $122,713.49 to $163,618 annually, subject to adjustment for qualifications, geographic pay differentials, and operational considerations. The position may be eligible for bonuses and commissions.

Requirements

Bachelor’s degree or higher in Statistics, Economics, Math, Computer Science, or a related analytical field, or equivalent experience.
At least 5 years of relevant experience producing models and engineering solutions in a production environment; 1+ year with a PhD or 3+ years with a master's degree.
Depth in predictive modeling, including regression, time series forecasting, and classification.
Strong statistical foundation, including experimentation and causal inference, and judgment to select methods appropriate to the question.
Ability to work independently, design validation approaches, and explain methodology to technical and non-technical reviewers.
Hands-on experience building with LLMs, such as agents, tool use and function calling, retrieval, or prompt-driven workflows; experience using tools such as Claude to accelerate development.
Experience with large, complex operational or HR datasets, including cleaning and structuring messy data and extracting data through SQL or APIs.
Fluency in SQL and strong proficiency in Python.
Ability to explain model results and agent behavior in plain language to non-technical HR and business leaders.

Responsibilities

Partner with HR leaders to identify forecasting or automation needs and build models and agents they can trust and act on.
Build and deploy predictive models using Python and SQL; monitor performance, retest with new data, and refine models when accuracy drifts.
Build AI agents that can query People data, reason over it, and take action, including the tool use, orchestration, and guardrails needed to handle sensitive workforce data safely.
Choose suitable analytical methods, use experimentation and causal inference when investigating cause, quantify uncertainty, and explain approaches to senior leadership.
Use LLMs to identify patterns in unstructured People data, such as survey comments and exit interview notes, and incorporate findings into models.
Develop familiarity with Lennar's people data, extract and structure data for modeling, identify data gaps, and partner with Data Engineering on supporting pipelines.
Explain statistical concepts, model results, and agent behavior to HR and business audiences without quantitative backgrounds.
Handle sensitive workforce and compensation data with discretion.