The Opportunity
Apartment List is looking for a Staff Data Scientist to help us attract and match the right renter with the right property at the right time.
In this role, you’ll work on some of the most important data science problems across Apartment List — from demand-side renter acquisition and marketing models like User Value Modeling, to supply-side partner models like Supply Value Score, to ranking, personalization, renter intent, and emerging Pathmaker AI work that coordinates the renter journey in real time.
Our Data Science team has a strong foundation. Over the last several years, we’ve delivered 40%+ incremental revenue growth through rigorously A/B tested machine learning models — and we’re still scratching the surface of what ML can do for renters, partners, and the business.
You’ll build zero-to-one models in areas still powered by heuristics and business rules, while also improving existing production ML systems across ranking, personalization, renter intent, and marketplace optimization. As a Staff-level IC, you’ll help shape the Data Science roadmap, raise the bar for modeling and experimentation, and partner deeply with Product, Engineering, Design, Analytics, Marketing, GTM and Growth to turn ambiguous opportunities into measurable impact.
What you’ll do
- Deeply understand customer, marketplace, and business problems through the lens of data, and translate that understanding into clear ML objectives, features, models, and measurement plans.
- Deliver and deploy end-to-end machine learning models, from problem framing and feature engineering through model development, experimentation, launch, monitoring, and iteration.
- Build zero-to-one models in areas where heuristics or business rules are still in place, and improve existing production models across demand, supply, ranking, personalization, renter intent, and marketplace optimization.
- Apply a strong statistical mindset to model development, experimentation, causal inference, tradeoff analysis, and decision-making.
- Lead ambiguous, high-leverage technical work: define scope, evaluate approaches, manage tradeoffs, and align stakeholders around a clear path forward.
- Partner closely with Product, Engineering, Design, Analytics, Marketing, GTM and Growth to build ML systems that drive renter value, property partner success, and business performance.
- Communicate ML opportunities, tradeoffs, and results clearly to technical and non-technical audiences, including senior stakeholders.
- Mentor and collaborate with other data scientists, helping raise the quality of our modeling, experimentation, and analytical practice.
- Thoughtfully leverage modern AI tools to improve productivity across coding, analysis, documentation, and workflow automation.
What you’ll bring
- 7+ years of industry experience, or equivalent experience, developing, deploying, and iterating on machine learning models in production.
- A degree in Computer Science, Computer Engineering, Mathematics, Statistics, Economics, Physics, or a related quantitative field.
- Deep proficiency in Python and SQL, with comfort working across the full model development lifecycle.
- Familiarity with standard ML libraries and frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or similar tools.
- Experience working with cloud platforms; GCP experience is preferred but not required.
- Experience with a broad set of statistical and machine learning methods to solve and optimize critical business problems and metrics.
- Strong technical and theoretical grounding in statistical learning, modeling, experimental design and analysis, and causal inference.
- Strong ability to work through feature engineering, feature selection, hyperparameter tuning, model evaluation, and model optimization.
- Ability to quantitatively research opportunities, define technical strategy, set clear scope, manage timelines, and drive measurable outcomes.
- Comfort communicating and collaborating with cross-functional audiences across Product, Engineering, Design, Analytics, Marketing, GTM and Growth and senior business stakeholders.
- Curiosity, judgment, and a hunger to dig into uncharted territory and make a meaningful impact.
Bonus points for
- Experience optimizing within a two-sided marketplace or similarly complex multi-stakeholder environment.
- Background in recommendation systems, ranking, personalization, search, or matching.
- Experience with performance marketing models, paid acquisition, supply-side optimization, or marketplace incentives.
- Familiarity with MLOps practices, ML engineering workflows, model monitoring, Airflow, dbt, or similar infrastructure.
- A master’s degree or PhD in a relevant quantitative field.
What’s in it for you
- Impact: Work on ML systems that directly shape the renter experience, property partner outcomes, and company performance.
- Ownership: Build and own models end-to-end, from ambiguous opportunity through production launch and iteration.
- Exceptional colleagues: Our hiring bar is high, and your teammates are talented, motivated, collaborative, and intellectually curious.
- Influence: Have a strong voice within R&D and across the business, helping shape product, marketplace, and company strategy through data science.
- A critical function: Help build and scale one of the most important technical capabilities at Apartment List.
- Culture: Work in a virtual-first environment that allows you to work from anywhere in the U.S.
Here's the Pay Range:
At Apartment List, we carefully consider a variety of factors to determine compensation for each position, including the role, level, and work. The US base salary range for this position is:
- Zone 1: $231,000 - $280,000 TTC (including $203,000 - $238,000 base salary) + equity
- Zone 2: $214,000 - $259,000 TTC (including $188,000 - $220,000 base salary) + equity
- Zone 3: $196,000 - $238,000 TTC (including $172,000 - $202,000 base salary) + equity
This reflects the compensation target for new hire salaries for the position across all US locations. Please note, the compensation details provided do not include benefits and perks that we offer.
We also rely on market indicators along with considering your work location, job related skills, experience and relevant education and training, to determine compensation that is fair and competitive for you. Apartment List will consider paying compensation near the higher of the range in exceptional circumstances, where candidates have the experience, credentials or expertise that would warrant such consideration. It is always our goal to hire exceptional talent and we would be happy to share more about compensation during the hiring process.