• Lead Data Analytics & Modeling: Drive data initiatives using both traditional machine learning and emergent AI technologies. Focus on pragmatic, non-generic applications that empower human decision-making and optimize our platform. • Data Pipeline Engineering: Work closely with the core engineering team to design, build, and maintain scalable data pipelines that support ML tooling, analytics, and high-velocity ad delivery systems. • Cross-Functional Collaboration: Leverage your software engineering proficiency to translate data science concepts into production-ready architecture, ensuring seamless integration between data models and backend systems. • Experimentation & Optimization: Design, evaluate, and operationalize experimentation frameworks for auction, pricing, and yield optimization. Building scalable methods to measure impact, validate model performance, and improve revenue driving decision systems. • Model Production & Monitoring: Productionize forecasting and optimization models by building backtesting, monitoring, and guardrail systems that ensure outputs are reliable, explainable, and safe to deploy in high output and delivery environments.

• Own the analytical function for the Discovery Mode ML squad, driving evaluation and continuous improvement of the models that power measurement and campaign optimization • Partner with ML engineers to develop evaluation frameworks and identify opportunities to improve model performance, reliability, and customer impact • Design and execute rigorous experiments to evaluate model quality, measure outcomes, and guide model development • Conduct deep-dive analyses to assess model performance and translate findings into clear, actionable recommendations for product and business stakeholders • Build, maintain, and evolve dashboards that track model health, customer metrics, and program performance • Collaborate with product managers, engineers, and cross-functional partners to align analytical priorities with squad goals and customer needs • Contribute to the broader Product Insights community by sharing best practices and helping raise the bar for analytics across Discovery Mode

• Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products. • Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring. • Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools. • Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact. • Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes. • Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time. • Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users. • Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact. • Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation. • Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices.

• Design and execute causal analyses of real-world health data to quantify the impact of medications and interventions on patient outcomes and costs • Develop, validate, and deploy predictive and causal models and AI tools into Arine's medication management platform • Partner with clinicians and HEOR researchers to frame questions as well-defined causal estimands and translate findings into clinical and operational decisions • Create presentations and reports communicating methods, assumptions, and results to the Data Science team and cross-functional stakeholders • Participate in critical reviews within the Data Science team on everything from methods and study design to implementation strategy and code