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 at scale.
This 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 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.

* The job posting highlights the most relevant / essential responsibilities and requirements of the role. It is not all-inclusive. There may be additional duties, responsibilities, and qualifications for this job.

Pay Range
$80,000 - $95,000

Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to achievements, skills, experience, or work location. The range listed is just one component of the compensation package offered to candidates.

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