Product Manager, Data Analytics

Equity Residential•Chicago, IL

2d•$106,228 - $132,785•Hybrid

About The Position

At Equity Residential, we're dedicated to creating thriving communities, and we invite you to be part of our team. Embracing values like Diversity, Sustainability, and Total Wellbeing, we foster a workplace culture of authenticity and collaboration. How We Deliver a Winning Performance: Question Authority Walk the Talk Share Knowledge Listen, not just Hear See the Glass Half Full Take Educated Risks Enjoy the Ride Share the Spotlight Do the Right Thing Test Your Limits The Product Manager, Data Analytics defines, delivers, and continuously enhances Equity Residential’s analytics and machine-learning–enabled products. This role sits at the intersection of data science, software engineering, and business strategy, translating analytical insights into scalable systems and partnering with technical teams to operationalize ML models into production-grade applications. The ideal candidate brings a blend of product management discipline, technical fluency in machine learning and data pipelines, and strong program management skills. This role collaborates closely with the Senior Software Engineer, data scientists, and business partners to build intuitive, reliable products that drive measurable impact on pricing, forecasting, and operational decision-making.

Requirements

Bachelor’s degree required; degree in Business, Computer Science, Engineering, Analytics, Economics, or related field preferred.
5+ years of experience in Product Management, Technical Program Management, or Engineering, with a focus on data, analytics, or platform development.
Strong technical working knowledge of machine learning models, data pipelines, model deployment workflows, and APIs—with hands-on experience partnering with data scientists and engineers.
Demonstrated ability to integrate ML-driven features into end-to-end production systems and evaluate model performance.
Proficiency with SQL, analytics environments, and BI tools (Power BI, Tableau, etc.).
Proven experience with Agile methodologies, backlog management, sprint execution, and iterative product delivery.
Experience defining and managing program-level KPIs, establishing review cadences, and leading data-driven decision processes.
Excellent verbal and written communication skills with ability to translate between technical and non-technical stakeholders.

Nice To Haves

Experience in Revenue Management, pricing optimization, forecasting, yield management, or similar analytical domains (real estate, hospitality, travel, etc.).

Responsibilities

Collaborates with business, analytics, and engineering teams to define the vision, strategy, and long-term roadmap for data and machine learning products.
Translates complex pricing, forecasting, and operational needs into clear product requirements, user stories, and acceptance criteria.
Leads prioritization across competing business and technical needs, managing backlogs and release plans.
Serves as product spokesperson and subject matter expert for analytics capabilities across the enterprise.
Works directly with data scientists to understand and refine model outputs, feature relevance, performance metrics, and constraints.
Applies strong technical knowledge of machine learning workflows, pipelines, APIs, and model monitoring to guide product direction and evaluate trade-offs.
Partners with the Senior Software Engineer – Data & Analytics Products to operationalize models into user-facing applications and enterprise systems.
Ensures all ML-driven product features emphasize explainability, compliance, and reliability.
Defines and tracks key KPIs (e.g., model accuracy, revenue lift, adoption, system reliability) to measure product performance and business impact.
Establishes a structured cadence for reviewing KPIs with cross-functional partners and initiating corrective actions or enhancements.
Drives successful delivery of milestones across pilots, MVP releases, and product iterations.
Leads retrospectives and drives continuous improvement of analytics workflows, model governance, and product execution processes.
Acts as the bridge between data scientists, software engineers, IT, Pricing, and Operations teams.
Facilitates workshops, demos, feedback sessions, and cross-functional updates to maintain alignment and transparency.
Communicates complex analytical concepts in a clear, business-relevant manner for stakeholders at all levels.
Supports change management, training, and adoption for new analytics capabilities across the organization.