Data Scientist - Multifamily Revenue Management

About the role

We are seeking a Data Scientist to bridge the worlds of multifamily revenue management and data science. ApartmentIQ is the market intelligence layer that operators, asset managers, and RM platforms rely on to price millions of apartment units, and this role sits at the center of making that intelligence sharper, more actionable, and more defensible.

Responsibilities

• Research and experiment with multifamily datasets to identify opportunities for new product features and pricing insights.
• Partner with product managers and engineers to define requirements and design data-driven capabilities for ApartmentIQ's revenue management products.
• Translate revenue management workflows into scalable tools and features — you should be able to sit in a pricing call and immediately understand what the revenue manager is trying to solve.
• Apply LLMs and other AI techniques to accelerate research, automate workflows, and unlock new insights from structured and unstructured market data.
• Design and run experiments to measure whether our data and recommendations actually drive better RM outcomes for customers.
• Create clear documentation, dashboards, and analyses to communicate findings and product opportunities.
• Stay current on multifamily industry trends, data sources, AI/ML advances, and pricing methodologies.

Qualifications

• 3–5 years of experience in or alongside multifamily revenue management — as a pricing analyst, RM advisor, asset manager, or in a strategy or analytics role at a multifamily operator or tech company.
• A genuine, working understanding of how pricing decisions get made: occupancy/rate tradeoffs, concession economics, net effective rent, renewal strategy, and how seasonality shapes leasing behavior.
• Entrepreneurial mindset: comfortable in a fast-paced, product-building environment.
• Experience applying LLMs or AI/ML techniques to accelerate research, automate analysis, or extract insights from data.
• Strong quantitative and analytical skills with proficiency in SQL.
• Familiarity with modern data science practices, including model building, experimentation, and validation.
• Strong business acumen and ability to translate operator workflows into product features.
• Excellent communication skills — comfortable explaining complex data insights to both technical and non-technical stakeholders.

Bonus Skills

• Proficiency in Python or similar.
• Experience building customer-facing analytical products or dashboards.