Multifamily Revenue Management Product Analyst

MavenAI is a profitable, growth-stage company building industry-leading martech and data products for the PropTech space. The Multifamily Revenue Management Product Analyst will bridge multifamily revenue management and data science, designing and delivering features that enhance pricing intelligence for the industry.

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
• Act as a subject matter expert on multifamily revenue management, ensuring the product reflects real-world operator needs
• 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

Requirements:

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