Apartment List is looking for a staff level data scientist to help us attract and match the right renter with the right property at the right time! In this role, you’ll have the opportunity to consider and balance multiple optimization objectives for renters, property management companies and revenue. To do so, you will need to understand the journey through the lens of data, and separate the important features from the noise. You’ll have the opportunity to lead work focused on machine-learning models that bring in renters, rank potential properties to best match renter preferences, and pinpoint the motivations and intent of renters. On your journey, you will partner with Product, Engineering, Design, Analytics and Growth to develop, implement, and evolve our machine learning models and practice.

Here’s what you’ll do as part of the team:

• Deeply understand the data and customer and business problems, in order to more closely align machine-learning model objectives and develop the best features and models to predict them
• Deliver and deploy end-to-end machine-learning models, build measurement plans, learn and iterate to drive results
• Apply a solid statistical mindset and best practices, in the collaborative process of model development, deployment and evolution
• Collaborate with other experienced data scientists
• Partner closely with Engineering, Product, Analytics, Growth and Design to develop and refine machine-learning models that drive the customer and business success
• Communicate broadly, clearly and often the opportunities and the results of applying ML - to stakeholders at all levels of the organization

Here are the skills and experience you’ll need to be successful:

• Experience with a broad set of statistical and machine learning methods to solve and optimize critical business problems and metrics
• A degree in Computer Engineering, Mathematics, Statistics, Economics, Physics or related quantitative field with at least 4 years of industry experience in developing machine-learning models, and ideally leading projects and small teams
• Strength both technically and theoretically in statistical learning and modeling, engineering, experimental design and analysis, causal inference
• Comfort and experience with communicating and collaborating with cross-functional audiences
• Ability to quantitatively research and act on technical strategies, define clear scope, scale and timeline projects
• Some mixture of SQL, Python and cloud (GCP prefered) experience will be essential
• Familiarity with standard ML libraries and managed solutions and a strong ability to work through feature engineering, selection, hyperparameter and model optimizations
• A hunger to dig into uncharted territory and make a huge impact

And here is what’s in it for you:

• An exceptional team: our hiring bar is absurdly high and your colleagues are talented, motivated, self- starters
• An incredible opportunity to build out a critical function
• A strong voice within R&D to influence our product and company strategy
• A virtual-first culture that allows you to work from anywhere in the US

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 $165,000 - $230,000 + bonus + equity, which 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.