• Analyze and interpret large, complex datasets to identify trends, patterns, and insights that support Zillow’s burgeoning rentals ecosystem. • Collaborate with product stakeholders to define key metrics, goals, and objectives, and develop data-driven strategies to achieve them. • Utilize causal inference techniques and novel experimental designs to evaluate the launches of pilot programs. • Mentor and coach junior team members on executing analytic requests and help unblocking through code reviews & best practice sharing. • Work closely with cross-functional teams, such as product, marketing, engineering, and corporate strategy, to ensure relevant insights are surfaced for decision making. • Collaborate with data engineering teams to design and optimize data pipelines and workflows to support efficient data collection, storage, and processing. • Effectively communicate complex analytical findings to diverse stakeholders, translating technical concepts into actionable insights and recommendations. • Dive into Zillow's internal and third party data (think Databricks, Snowflake, Big Data) to develop solutions that will help move the business forward and enable better decisions at a rapid speed.

• Going from 0 to 1 - Build and productionize machine learning models. • Design and execute product experiments to help improve Wealthsimple’s client experience. • Work with cross-functional teams within Growth, Product, Marketing, and Engineering. • Translate experimental results into products and solutionsSpearhead data modelling strategyEnsure models are well tested, documented and maintained. • Believe that simple is better, Occam's razor is your friendTake ownership and ship it; release incrementally and iteratively. • Teach and learn from your teammates. We value making others successful.

• Be innovative. Aim to improve consumer experience for sustainable growth • Build and enhance machine learning, statistical and causal models for product search, ranking and recommendation that support various business goals • Work closely with product and engineering team to develop and deploy solutions with cross-functional support • Build models that support real-time events and internal stakeholder decisions across the business • Present work to business and engineering leadership • Identify gaps in existing data, create data product specs, and work with Engineering teams to implement enhanced data solutions • Are committed to automation and productionalized solutions whenever possible

• Own the end-to-end development and operation of deep learning–based ETA prediction systems—building features, training/testing models, running experiments, monitoring and explaining performance in production. • Collaborate with our business, product, and engineering partners across the organization to vision and deliver data science solutions for practical business problems. • Deeply understand our users, our products, and our data to identify opportunities for creating machine learning, optimization and causal inference solutions to deliver a better customer experience and make our business more efficient. • Build data driven, robust and replicable data science models. • Use experimentation and causal inference to measure the impact of our models on user engagement and business metrics.