Senior Data Scientist (ML Engineering)

The Senior Data Scientist will be responsible for designing, developing, and optimizing machine learning, AI, and decisioning solutions that drive customer, risk, and operational outcomes across Personal and Private Banking (PPB) and Digital channels. This role involves applying advanced analytics, machine learning, and AI techniques to solve complex business problems, enhance customer experiences, and support intelligent decision-making, with a strong emphasis on feature engineering, model development, experimentation, and decision science leveraging the Databricks platform.

Key Responsibilities:

• Collaborate with PPB, Digital, Product, and Risk teams to identify and prioritize high-value analytical opportunities.
• Design, develop, train, and optimize machine learning models using Databricks, MLflow, and distributed compute environments.
• Develop models for customer propensity, next best action, next best product, customer value, retention, and engagement.
• Develop risk, collections, fraud, and operational models to improve business performance and decision quality.
• Build and maintain reusable feature pipelines using Databricks Feature Engineering and Delta tables.
• Define, create, and govern reusable enterprise features for inclusion within the Databricks Enterprise Feature Store, establishing standards for feature definitions, quality controls, lineage, and monitoring across business domains.
• Perform feature engineering, selection, and importance analysis to enhance model performance and reuse.
• Develop optimization and decisioning models to support customer engagement, product recommendation, and operational decision strategies.
• Conduct exploratory data analysis, statistical analysis, hypothesis testing, and model validation using Databricks notebooks and workflows.
• Execute model tuning, calibration, challenger model development, and performance benchmarking.
• Monitor model performance, stability, drift, and business outcomes, recommending improvements as needed.
• Develop and implement Generative AI use cases, including customer support, document intelligence, and knowledge-based assistants.
• Work closely with ML Engineers to productionize models and features into enterprise platforms and decisioning systems.
• Produce comprehensive model documentation, validation reports, and governance artifacts in compliance with model risk management requirements.
• Mentor junior Data Scientists and contribute to the development of modeling standards, reusable frameworks, and analytical best practices.

Required Skills and Qualifications:

• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematical Statistics, Actuarial Science, Mathematics, Econometrics, or a related quantitative field.
• Proven experience developing machine learning models on the Databricks platform.
• Strong understanding of feature engineering, feature selection, and feature optimization techniques.
• Experience building and governing reusable enterprise features within Feature Stores.
• Proficiency in Python and SQL.
• Solid experience with Machine Learning and Statistical Modeling.
• Demonstrated expertise in Predictive and Prescriptive Analytics.
• Experience with Customer Decisioning and Optimization techniques.
• Hands-on experience with Experimentation and Model Validation.
• Knowledge of Generative AI and Large Language Models (LLMs).
• Skills in Data Visualization and Storytelling.
• Experience in Stakeholder Management.
• Experience working with large-scale customer, behavioral, transactional, and digital datasets.
• Experience designing model monitoring and performance measurement frameworks.
• Ability to translate business problems into analytical solutions and measurable business outcomes.
• Strong stakeholder engagement, communication, and business consulting skills.
• Experience delivering end-to-end data science use cases from concept to production deployment.
• Self-driven and able to operate effectively in a fast-paced, outcome-focused environment.

Preferred Qualifications:

• Master's or Doctorate degree in a relevant quantitative field.
• Databricks Machine Learning Engineer Certification.
• Databricks Data Engineer Certification.
• Microsoft Azure AI Certifications.
• SAS Data Scientist Certifications.
• AWS or Google Cloud AI/ML Certifications.
• Machine Learning, Artificial Intelligence, or Data Science certifications from recognized providers such as Microsoft, Databricks, SAS, Coursera, or DeepLearning.AI.
• Experience using Databricks MLflow for model tracking, experimentation, and governance.