[Hiring] Senior Manager, Data Scientist @Greystar

Role Description

Greystar’s D2AI team is responsible for the platforms, processes, and practices that power AI across the organization. This role goes beyond traditional delivery—your decisions influence how data is transformed into intelligent, scalable solutions used by teams company‑wide.

• Lead the data science function embedded within the GPS product team.
• Build models, lead a small team of data scientists and ML engineers.
• Work directly with Product, Engineering, and business stakeholders to ship intelligence into a product used by Greystar’s leadership, property managers, owners, and investors.

Key Responsibilities

• Build and Ship Predictive and Prescriptive Models
  • Design, build, and deploy models that power GPS — including asset performance scoring, NOI forecasting, renewal probability, lease pricing optimization, and anomaly detection across portfolio KPIs.
  • Develop benchmarking and comparative analytics capabilities.
  • Own the full model lifecycle within GPS: problem framing, feature engineering, model development, validation, deployment, monitoring, and iteration.
• Integrate AI into the GPS Product Experience
  • Partner tightly with GPS Product and Engineering to embed data science outputs directly into the product.
  • Design and implement LLM-powered features within GPS.
  • Build and maintain feature stores and serving infrastructure.
  • Define and track model performance metrics that tie directly to business outcomes.
• Lead and Grow a Data Science Team
  • Manage and mentor a team of 2–4 data scientists and ML engineers.
  • Set technical standards for the team.
  • Prioritize the data science backlog in partnership with Product Management.
  • Recruit and retain top data science talent.
• Partner Across the Organization
  • Work with Data Engineering to ensure GPS has access to clean, validated, timely data.
  • Collaborate with the Chief Data Scientist on methodology, model governance, and reusable frameworks.
  • Translate business questions into data science problems.
  • Present model results, insights, and recommendations to senior business leaders.

Qualifications

• 7+ years of applied data science or ML engineering experience, with at least 2 years managing a data science team.
• Strong track record of building and deploying models in production.
• Deep expertise in supervised/unsupervised learning, time series forecasting, regression, classification, and anomaly detection.
• Strong Python skills and fluency with the modern ML stack.
• Experience with feature engineering at scale.

Requirements

• Hands-on experience with LLMs in production.
• Understanding of how to integrate AI/ML outputs into product UX.
• Awareness of model governance: bias detection, fairness, explainability, and responsible AI practices.
• Experience working as an embedded data scientist within a product team.
• Ability to translate ambiguous business problems into structured data science work.
• Comfort presenting to non-technical senior stakeholders.

Tools & Technologies

• Python, SQL, Databricks/Spark for model development at scale.
• MLflow, Weights & Biases, or similar for experiment tracking and model registry.
• Azure ML, SageMaker, or similar cloud ML platforms.
• LLM APIs (OpenAI, Anthropic), vector databases, and retrieval frameworks.
• Git, CI/CD for ML pipelines, and infrastructure as code.

Benefits

• Competitive Medical, Dental, Vision, and Disability & Life insurance benefits.
• Generous Paid Time off: 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays.
• 6-Week Paid Sabbatical after 10 years of service.
• 401(k) with Company Match up to 6% of pay after 6 months of service.
• Paid Parental Leave and lifetime Fertility Benefit reimbursement up to $10,000.
• Employee Assistance Program.
• Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans.
• Charitable giving program and benefits.