Senior Manager, Data Science

• Lead and develop a team of Data Scientists through coaching, mentorship, technical guidance, and career development
• Partner with business leaders to identify and prioritize high-value machine learning, artificial intelligence, and advanced analytics opportunities
• Translate complex business problems into analytical frameworks, models, and actionable insights
• Oversee the end-to-end data science lifecycle, including problem formulation, feature engineering, model development, validation, deployment, monitoring, and optimization
• Guide predictive, prescriptive, and generative AI solutions to improve business performance, efficiency, customer experience, and risk management
• Collaborate with data engineering, technology, and platform teams on scalable, production-ready solutions
• Establish model governance, validation, explainability, documentation, and monitoring practices
• Evaluate emerging AI, machine learning, and data science techniques and recommend applications
• Present analytical findings, recommendations, and business cases to senior executives and stakeholders
• Drive experimentation and innovation through proofs of concept, pilots, and test-and-learn initiatives
• Ensure responsible and ethical use of AI in accordance with regulatory requirements, governance frameworks, and internal policies
• Manage portfolio planning, resource allocation, and delivery execution across concurrent initiatives
• Hire talent, set goals, develop staff, manage performance and compensation decisions, and handle disciplinary actions as required
• Oversee a large and/or highly complex analytical function
• Partner with leadership on portfolio and financial management, strategic roadmaps, and long-term goals
• Lead enterprise analytics solutions for customers and collaborate with business partners on ad hoc analysis
• Manage team workload, assign data requests, develop business plans, identify growth opportunities, and report risk issues
• Maintain alignment with enterprise frameworks, regulatory requirements, controls, remediation plans, and risk appetite
• Build and retain an engaged, diverse, inclusive, and high-performing team
• Develop annual and long-term plans aligned with enterprise priorities

• Undergraduate degree or advanced technical degree preferred
• Graduate's degree preferred with either progressive project work experience, or 7+ year of relevant experience; higher degree education and research tenure can be counted
• 3+ years deep expertise in machine learning algorithms, statistical modeling, predictive analytics, and experimentation methodologies
• 7+ years of experience applying advanced analytics, machine learning, artificial intelligence, or statistical modeling techniques in complex business environments
• Advanced proficiency in Python and common data science libraries such as Pandas, Num Py, Scikit-learn, Tensor Flow, PyTorch, XGBoost, or similar frameworks
• Experience developing and deploying Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), or Agentic AI solutions
• Experience operationalizing machine learning models within enterprise environments using MLOps practices and cloud platforms
• Strong understanding of model governance, explainability, fairness, bias mitigation, and responsible AI practices
• Experience leading cross-functional AI and analytics initiatives involving business, technology, risk, and governance stakeholders
• Demonstrated ability to communicate complex analytical concepts to executive and non-technical audiences
• Experience in financial services, banking, risk management, audit, regulatory, or highly regulated industries
• Familiarity with cloud-based analytics environments such as Azure, AWS, or Google Cloud
• Experience managing a portfolio of data science initiatives and delivering measurable business value
• Occasional domestic travel required
• Must perform sedentary work, operate standard office equipment, sit, and concentrate for long periods continuously

Demonstrates expertise in machine learning, artificial intelligence, and advanced analytics, with a strong focus on model governance, ethical AI practices, and delivering business value through data-driven insights. Proven ability to lead and develop high-performing teams while collaborating with cross-functional stakeholders to drive innovation and operational excellence.

• Machine Learning Algorithms
• Advanced Analytics
• Python Proficiency
• Model Governance
• Generative AI Solutions

• Statistical Modeling
• Predictive Analytics
• Experimentation Methodologies
• Feature Engineering
• MLOps Practices
• Data Science Libraries
• Cloud Platforms
• Data Science Lifecycle Management
• Analytical Frameworks
• Portfolio Management

• Coaching
• Mentorship
• Communication
• Collaboration
• Leadership

• Financial Services
• Risk Management
• Regulatory Compliance
• Governance Frameworks
• Business Analytics

• Pandas
• Num Py
• Scikit-learn
• Tensor Flow
• Py Torch
• XGBoost
• Azure
• AWS
• Google Cloud
• Data Engineering Tools