• Design and implement end-to-end ML pipelines • Develop and experiment with Deep Neural Network architectures • Translate ambiguous business requirements into technical solutions • Work with large datasets for acquisition, preprocessing, and analysis • Ensure code quality and maintainability • Collaborate with cross-functional teams to integrate ML solutions into production systems • Utilize CI/CD, version control, and automated testing in ML projects

• Under guidance of more senior data scientists, own the development and deployment of machine learning models to support operational and clinical use cases. • Perform ad-hoc data analyses related to outputs or alternative use-cases for existing models (e.g. summarize outputs for reporting purposes) • Work with program owners to understand the goals, key performance measures, and operational details of programs and processes to ensure models are aligned with program intent • Create and present proposals for machine learning solutions to program owners with the aim of ensuring buy-in to proposed solution. **Please note that employment will be contingent upon providing documentation verifying your legal work authorization in the country of residence, in accordance with applicable law.**

• Collaborate closely with Data Engineers and business stakeholders to define and validate curated datasets optimized for analytics and AI use cases. • Specify and document data requirements, feature definitions, and aggregation logic for downstream analytical and machine learning applications. • Validate data completeness, consistency, and statistical soundness of serving-layer datasets. • Provide continuous feedback to data engineering teams on data model usability, performance, and analytical fitness. • Perform exploratory data analysis (EDA) to identify patterns, anomalies, and data quality issues. • Build and test baseline machine learning models and analytical prototypes using curated datasets. • Contribute to the design and implementation of lightweight ML pipelines (training, evaluation, inference) aligned with platform standards. • Ensure features and models are reproducible, versioned, and prepared for future operationalization in production environments.

• Own diagnostics, insights, and tuning for AI Decisioning campaigns • Explain why AI Decisioning is driving lift using counterfactuals, incrementality breakdowns, and cohort analysis. • Debug performance issues, iterate on reward functions, and ensure the agent’s recommendations align with customer goals. • Investigate experiment setups (send volumes, reachability, channel constraints) and surface actionable recommendations. • Work deeply with data in notebooks and customer warehouses • Pull down historical data to run exploratory analyses using Polars / Pandas in Jupyter notebooks • Modify and improve customer feature matrices to unlock deep personalization. • Conduct deeper warehouse-level SQL analyses when insights aren’t available in the UI. • Build lightweight tooling that enables scale • Create templates, notebooks, scripts, and repeatable workflows that improve how we analyze performance across customers. • Identify systemic gaps and influence the direction of ML reporting and introspection. • Communicate ML concepts clearly to non-technical stakeholders • Present model insights and recommendations to marketers, analysts, and executives. • Explain how the decision engine handles cold start, message transfer learning, exploration vs. exploitation, and more. • Partner closely with Solutions Consultants to identify and drive new opportunities for uplift.