Senior Data Scientist
On-siteCentral, Louisiana, United States
Job Summary
Design and deploy advanced ML models for cross-sell, upsell, deep-sell, and look-alike use cases to maximize customer lifetime value and revenue per customer. Develop customer segmentation, propensity scoring, next-best-action, and recommendation engines that inform personalized engagement strategies. Automate end-to-end model lifecycle using AWS SageMaker Pipelines and MLOps best practices. Leverage LLMs and AWS Bedrock for insight generation, automated commentary, and agentic workflows. Translate model outputs into actionable business recommendations for product managers, marketing leads, and senior leadership. Conduct rigorous A/B testing and champion-challenger frameworks to measure model impact on business KPIs. Collaborate with data engineering teams to ensure robust feature pipelines and data quality. Mentor junior data scientists and establish best practices for model development, documentation, and reproducibility.
Required Qualifications
- Master's or PhD in Statistics, Mathematics, Computer Science, Economics, or a quantitative discipline
- 7+ years of hands-on experience in data science with a strong focus on business/commercial analytics in banking, financial services, or consumer platforms
- Proven track record building and deploying production-grade predictive models (propensity, recommendation, segmentation, LTV)
- Deep expertise in statistical methods: regression, classification, ensemble methods, Bayesian inference, time-series analysis
- Strong proficiency in Python (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and SQL
- Hands-on experience with AWS SageMaker (Training Jobs, Endpoints, Pipelines, Feature Store) for model automation and deployment
- Experience with LLM/GenAI tools (Claude, GPT) for prompt engineering, RAG architectures, and AI-assisted analytics workflows
- Exceptional business acumen - ability to connect data patterns to revenue, cost, and customer experience outcomes
- Strong communication skills to present complex findings to non-technical stakeholders
Desired Qualifications
- Experience with AWS Bedrock for building GenAI-powered applications and agents
- Familiarity with causal inference methods and uplift modelling for campaign optimisation
- Experience in retail banking products (cards, loans, deposits, wealth) and customer lifecycle analytics
- Knowledge of MLOps frameworks, CI/CD for ML, model monitoring, and drift detection
- Experience building real-time scoring systems and feature engineering at scale
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