•Analyze and interpret complex data sets from various sources, utilizing advanced data analytics skills to uncover patterns and provide insightful reporting in support of operational and strategic initiatives, across rewards, redemption, exception, and partner performance data, and build, validate, and interpret detection models on large card datasets using unsupervised methods for pattern discovery and supervised methods as labeled outcomes accumulate.
•Develop and implement automation strategies, leveraging systems architecture knowledge to optimize processes and drive departmental efficiency, including the text classification pipelines behind complaint and feedback analysis, their ground truth sets and per-category accuracy, and monitoring of deployed models and rules for drift, recalibration, and retraining.
•Coordinate cross-functional collaboration, working effectively with diverse teams across the organization to align efforts, share knowledge, and drive the successful implementation of business strategies, defining which metrics, scores, and explanations reach each user group, shaping the dashboards and summaries that carry them, and presenting results to technical and senior non-technical audiences.
•Utilize strategic thinking to evaluate potential scenarios, assess risks, and make informed decisions that directly impact departmental outcomes, translating model output with business partners into decision rules, thresholds, and scoring bands, and recommending the right approach per problem across rules, statistics, and machine learning, weighing accuracy, explainability, and governance.
•Provide coaching to team members, empowering them to take ownership of their work while ensuring objectives are met efficiently and effectively, designing how operational decisions and case outcomes are captured as labeled data so each model version improves on the last, and documenting objectives, data sources, methodology, assumptions, and limitations to a standard that withstands independent validation and audit review.
Required qualifications, capabilities, and skills
•Demonstrated proficiency in developing and implementing automation strategies, with a strong understanding of systems architecture, including Python for modeling (pandas, NumPy, scikit-learn) and advanced SQL with window functions and query optimization on a cloud platform such as Snowflake, Databricks, BigQuery, Redshift or similar.
•3+ years of experience in data science, advanced analytics, or a related quantitative role.
•Proven ability to coordinate cross-functional collaboration, with experience in working with diverse teams across an organization, translating technical findings into concise business narratives for senior audiences, and building user-focused reporting in Tableau, Power BI, or Looker.
•Advanced strategic thinking skills, with a track record of evaluating potential scenarios, assessing risks, and making informed decisions, including hands-on machine learning across classification, anomaly detection, and clustering, and the judgment to know when rules or simpler statistics beat machine learning, and what each implies for governance.
•Experience in providing coaching and technical guidance to team members, with a focus on empowering individuals and ensuring efficient achievement of objectives, supported by sound evaluation practice across model explainability, class imbalance, data leakage, and validation design, and familiarity with version control and code review (Git, Bitbucket, or similar).
•Provide quality service to customers through continuous communication, with strong written communication including methodology and process documentation.
•Understand software delivery lifecycle and have skills in industry-standard methodologies and related tasks, including hands-on ETL and data preparation workflow experience in an established platform, and handling of personally identifiable or regulated data under defined access controls.
