Lead the design, development, validation, and deployment of production-grade machine learning models for customer segmentation, propensity scoring, behavioral prediction, and related customer intelligence use cases.
Own the full model lifecycle for complex workstreams, including problem framing, data exploration, feature engineering, training, validation, deployment readiness, monitoring, retraining, and performance interpretation.
Define evaluation frameworks and success metrics that connect model performance to client, product, and business outcomes.
Partner with product and engineering leaders to shape modeling roadmaps, clarify trade-offs, and prioritize high-impact analytical solutions.
Provide mentorship, code review, and technical guidance to data scientists, analysts, and adjacent technical partners.
Establish and promote best practices for model documentation, reproducibility, explainability, fairness, governance, and compliance review.
Collaborate with MLOps, engineering, and data platform teams to ensure scalable, reliable, and maintainable model deployment patterns.
Communicate complex modeling concepts, analytical findings, and business implications clearly to technical and non-technical stakeholders, including leadership and clients.
Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or a related quantitative field, with 5–8+ years of progressive experience in applied machine learning, data science, or advanced analytics and a demonstrated track record of delivering impact in production environments.
Expert proficiency in Python, including libraries such as pandas, scikit-learn, XGBoost, or LightGBM, and SQL is required. Proven experience developing and deploying machine learning solutions end-to-end, including problem framing, validation, monitoring, lifecycle management, and stakeholder adoption, is required.
Strong foundation in statistical modeling, supervised and unsupervised learning, customer analytics, experimental design, A/B testing, model validation frameworks, and responsible AI practices is expected.
Experience with cloud platforms, preferably AWS, modern data ecosystems, Snowflake, MLOps tooling, and production deployment patterns is expected. Strong stakeholder management and communication skills are required, including the ability to engage with leadership and clients.
