• 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.

• Exposing Insights: As a Data Scientist, your goal is not to just display data, but turn it into information. You will produce analysis reports and diagnostic models to try and discover hidden relationships and patterns between our data and metrics of interest. • Evaluate & Produce Quality: Good code is reviewed code. You will be involved in ensuring your and your teammates’ code is free from errors, bias, and is easy to understand. • Data Engineering: We are a newer team at a growing company, and you’ll need to do a lot of your own data engineering. Gather, clean, and preprocess data from various sources, ensuring accuracy and consistency. Perform feature engineering to generate new variables or transform existing ones to improve the quality and usefulness of the dataset. Tie all these tasks together in a pipeline and deploy on cloud based infrastructure. • Predictive Modeling: Develop, validate, and deploy predictive models using machine learning algorithms and statistical techniques, such as regression, classification, clustering, time series forecasting, and optimization. • Generative Modeling: Use a combination of open source and paid technologies to produce abstractions & novel features for other applications. • Continuous Improvement: Data Science is a quickly moving field and you’ll need to keep up to date. You will need to keep abreast of the latest developments in data science, machine learning, and reporting technologies, while incorporating them into your work when appropriate. Participate in knowledge-sharing sessions to contribute to the growth and development of others.

• Own the full analytics lifecycle: requirements discovery, data modeling, transformations, BI delivery, validation, and ongoing optimization • Translate business and clinical questions into scalable, reliable data and analytics solutions • Actively contribute at a technical level: advanced SQL, data modeling, dbt transformations, and analytics workflows • Design, build, and optimize ETL/ELT pipelines using AWS Redshift, dbt, and related tools • Ensure data quality, performance, scalability, and reliability across the analytics stack • Oversee and build customer-facing dashboards and reports that turn complex healthcare data into clear, actionable insights • Act as the primary analytics partner for clients, leading discussions, demos, and presentations • Communicate insights and recommendations to both technical and non-technical stakeholders, including executive audiences • Lead, mentor, and support BI analysts and analytics engineers • Establish best practices for SQL, data modeling, dashboard design, documentation, and code reviews • Champion data governance, security, and quality standards • Identify opportunities to improve metrics, automation, tooling, and analytics processes based on client and business needs