At Apartment List, we carefully consider a variety of factors to determine compensation for each position, including the role, level, and work. The US Total Target Compensation (TTC) for this position is: Zone 1: $172,700 – $209,000 TTC + Equity Zone 2: $159,700 – $193,000 TTC + Equity Zone 3: $146,800 – $177,000 TTC + 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.
Staff Data Scientist
Location
United States
Posted
21 days ago
Salary
$196K - $280K / year
Seniority
Lead
Job Description
Staff Data Scientist
Apartment List
Role Description 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. - Work on demand-side renter acquisition and marketing models like User Value Modeling. - Develop supply-side partner models like Supply Value Score. - Focus on ranking, personalization, renter intent, and emerging Pathmaker AI work. - Build zero-to-one models in areas still powered by heuristics and business rules. - Improve existing production ML systems across ranking, personalization, renter intent, and marketplace optimization. - Help shape the Data Science roadmap and partner with various teams to turn ambiguous opportunities into measurable impact. Qualifications - 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. - Strong technical and theoretical grounding in statistical learning, modeling, experimental design and analysis, and causal inference. - Curiosity, judgment, and a hunger to dig into uncharted territory and make a meaningful impact. Requirements - 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. - 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. Benefits - Work on ML systems that directly shape the renter experience, property partner outcomes, and company performance. - Build and own models end-to-end, from ambiguous opportunity through production launch and iteration. - Collaborate with talented, motivated, and intellectually curious colleagues. - Have a strong voice within R&D and across the business, helping shape product, marketplace, and company strategy through data science. - Work in a virtual-first environment that allows you to work from anywhere in the U.S.
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