Associate Decision Scientist

Associate Decision Scientist

A Brief Overview The Associate Decision Scientist supports the ongoing maintenance, monitoring, and incremental improvement of propensity models that are in production; these models determine a customer's likelihood of certain actions. These models power decisioning across owned channels including email, SMS, and push, enabling personalized data-driven customer engagement s role works closely with the customer analytics team and marketing partners to keep existing models accurate, well-monitored, and performing as expected.

You would partner with the marketing channel owners to provide insight into decisions and translate the data to actionable insights.

What You Will Do

• Maintain and optimize existing propensity and response models (purchase, churn, reactivation, category affinity, channel, promotion, and creative recommendations) to ensure ongoing accuracy and effectiveness at the individual customer level.
• Monitor model performance and health, including drift detection, retraining cycles, validation, and continuous improvement of marketing decisioning models.
• Support model deployment and operations within real-time and near-real-time marketing environments, partnering with Marketing Technology teams to maintain scoring and decisioning capabilities.
• Execute and analyze A/B, multivariate, holdout, and incrementality tests to evaluate model performance and measure marketing-driven lift.
• Translate model outputs into actionable marketing strategies by partnering with campaign and CRM teams to develop audience segments, suppression lists, and treatment assignments.
• Apply and support marketing optimization frameworks that balance short-term revenue objectives with long-term customer lifetime value (CLV) growth.
• Develop, maintain, and enhance customer-level predictive features and feature stores using transactional, behavioral, engagement, loyalty, and third-party data sources.
• Analyze customer behavior and segmentation data to deepen understanding of loyalty tiers, shopping occasions, affinities, channel responsiveness, and promotional sensitivity.
• Develop, maintain, and troubleshoot production-quality analytics solutions, including Python/R model code, feature engineering workflows, and SQL-based data pipelines.
• Prepare, validate, and manage modeling datasets and scoring processes to support reliable model execution and reproducibility.
• Document model methodologies, assumptions, performance results, and governance requirements to ensure transparency, compliance, and operational continuity.
• Communicate model performance and analytical insights to business stakeholders, translating technical findings into clear recommendations while supporting cross-functional collaboration and ongoing professional development.

Education Qualifications

• Bachelor's Degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or related quantitative field. Required
• Master's Degree in Statistics, Data Science, Operations Research, Machine Learning, or related field. Preferred

Experience Qualifications

• 1-2 years Applied data science or quantitative analytics, with exposure to predictive modeling and machine learning in a business context. Required
• 1+ years Working with or supporting customer-level models in a retail, e-commerce, or CRM/loyalty marketing context. Preferred
• Working with models in production environments; familiarity with CDP platforms (e.g., Salesforce Marketing Cloud, Adobe, Braze), a plus.

Skills and Abilities

• Solid proficiency in Python and/or R for statistical modeling, machine learning, and data manipulation.
• Working knowledge of supervised and unsupervised ML algorithms: gradient boosting (XGBoost, LightGBM), neural networks, clustering, and survival models.
• SQL skills for complex data extraction and feature engineering from large enterprise datasets.
• Developing ability to frame business problems into structured analytical approaches, with growing comfort working within existing model designs.
• Foundational understanding of customer lifecycle economics, CLV modeling, and the mechanics of CRM and loyalty marketing.
• Basic familiarity with incrementality, experimental design, and the distinction between correlation and causal lift.
• Ability to communicate quantitative concepts and model results clearly to non-technical stakeholders.
• Willingness to collaborate cross-functionally and communicate analytical findings clearly to marketing and business partners.
• Eagerness to learn and grow within a collaborative data science team, with a strong sense of ownership and attention to detail.
• Ability to manage time and workload effectively with flexibility to shift priorities based on business need.
• Exposure to or coursework in reinforcement learning, multi-armed bandit, or contextual bandit approaches for real-time decisioning is a plus.
• Familiarity with cloud-based data environments (Snowflake, Databricks, AWS, GCP); exposure to MLOps or model deployment pipelines is a plus.

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