• Responsible for refactoring the Optimization algorithm written in Python using Object Oriented Programming • Work on the latest applications of data science to solve business problems in the Supply chain and optimization space of Retail and/or CPG. • Utilize advanced statistical techniques and data science algorithms to analyze large datasets and derive actionable insights related to Pricing Optimization. • Develop and implement predictive models and optimization algorithms to improve inventory management, reduce stockouts, and optimize resource allocation across the supply chain. • Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions. • Design and execute experiments to evaluate the effectiveness of different replenishment strategies and allocation policies. • Monitor and analyze key performance indicators (KPIs) related to replenishment and supply chain allocation, and provide recommendations for continuous improvement. • Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization. • Collaborate, coach, and learn with a growing team of experienced Data Scientists.

• Churn & retention modeling: Build churn/survival models and lifecycle-state models that flag at-risk customers early and anticipate where each customer is heading. • Intervention & reason understanding: Power the retention interventions that act on that risk — save flows, skip/pause deflection, lifecycle messaging — and classify why customers churn so the response fits the reason rather than being generic. • Resurrection & win-back: Develop propensity models and personalized experiences that bring churned customers back. • Personalization & Next-Best-Action: Decide the right action, offer, and message per customer across product surfaces, with an uplift layer measuring incremental impact. • Offer & promo optimization: Determine who gets an incentive, when, and at what value — maximizing retained revenue without over-discounting. • Production & MLOps: Own the full model lifecycle and ship models into the systems where they act; handle monitoring, retraining, and drift. • Experimentation & incrementality: Design experiments and uplift measurement so we intervene on movable customers and not on those who'd retain anyway.

• Own and drive end-to-end ML projects that directly impact product growth, engagement, and monetization, from problem framing to deployment and measurement • Build and maintain ML models such as user segmentation, churn prediction, and recommendation systems to surface actionable opportunities for the product • Partner with Product Managers to translate business questions into well-defined analytical or modeling problems with clear success metrics • Conduct deep-dive analysis on user behavior, funnels, cohorts, and journeys to identify growth opportunities and inform product strategy • Design, execute, and analyze A/B and multivariate experiments with a strong focus on revenue, retention, and engagement impact • Develop and maintain key product KPIs, dashboards, and monitoring metrics to track model performance and product outcomes • Ensure accurate event tracking and data instrumentation for new and existing product features • Translate complex analytical and modeling findings into clear, actionable recommendations for both technical and non-technical stakeholders • Act as a thought partner and data leader within product teams, fostering a strong data-driven and experimentation culture

• Serve as the embedded data science partner to Marketing, bringing rigorous, data-driven decision-making to a team that today lacks dedicated analytical support. • Stand up and own the marketing measurement foundation — event tracking architecture, unified attribution windows, and KPI frameworks across mobile (MMP) and web (GA) — so channels can be compared apples-to-apples. • Build and maintain the core models that drive spend decisions: LTV, ROAS, CAC/payback, and retention; translate them into channel strategy and budget allocation recommendations. • Design, run, and read out experiments and A/B tests across acquisition and the customer lifecycle, and set the experimentation standard for the marketing org. • Analyze the full funnel (acquisition → activation → engagement → retention → monetization), surface the highest-impact growth opportunities, and size them. • Build forecasting and opportunity-sizing models to inform planning and resource allocation. • Develop self-serve dashboards and recurring reporting that give Marketing and leadership clear visibility into performance. • Partner with Data Engineering and MMP/ad-platform teams to ensure data quality and measurement accuracy, and prioritize fixes. • Translate analysis into clear, persuasive recommendations, and influence marketing investment decisions with stakeholders up to leadership.