Data Scientist Lead - Bank AI/ML

About the Role

Key Responsibilities

• Gathers, interprets, and manipulates complex structured and unstructured data to enable advanced analytical solutions for the business.
• Leads and conducts advanced analytics using machine learning, simulation, and optimization to deliver business insights and achieve objectives.
• Guides team on selecting appropriate modeling techniques considering data limitations and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes and peer reviews technical documents for knowledge sharing, risk management, and technical review audiences.
• Partners with business leaders to identify needs and propose analytical projects to generate business value.
• Builds and maintains a library of reusable, production-quality algorithms and supporting code.
• Translates business requests into analytical questions, drives analysis, and presents outcomes to non-technical stakeholders.
• Leads project portfolio management, anticipates issues, and escalates risks as needed.
• Establishes methodologies for deploying analytical assets in collaboration with Data Engineering and IT.
• Interacts with peers and management to stay updated on innovative techniques and technologies.
• Serves as a mentor to data scientists in modeling, analytics, and communication skills.
• Participates in enterprise efforts to evolve data science technologies and culture.
• Ensures risks are identified, measured, and controlled in accordance with policies.

Requirements

• Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or similar quantitative field, or 4 years of related experience.
• 8 years of experience in predictive analytics or data analysis, or a Master's/PhD with 6 years of relevant experience.
• 6 years of experience in training and validating advanced analytics models.
• 4 years of experience with scripting languages such as Python or R for statistical analysis and modeling.
• Strong experience in querying and preprocessing data from structured and unstructured databases using SQL, HQL, NoSQL, etc.
• Experience working with various data file types including JSON/XML, text, images, etc.
• Excellent skills in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• Ability to assess and articulate regulatory implications of modeling efforts.
• Project management experience with risk and impediment management.
• Expertise in classical supervised modeling techniques (linear/logistic models, decision trees, SVMs, etc.).
• Expertise in unsupervised modeling techniques (clustering, neighbors algorithms, DBSCAN, etc.).
• Experience mentoring junior staff and communicating technical ideas to non-technical audiences.
• Proven track record of communicating results to senior management.

Nice to Have

• Extensive model building experience in banking covering various AI/ML and statistical approaches.
• Hands-on experience preparing data and code for modeling.
• Experience with the entire model lifecycle from conceptualization to monitoring.
• Experience working directly with clients or internal business partners.
• Leadership experience managing client or customer relationships, including with senior leadership.

Qualifications

• Educational background in a quantitative field or equivalent experience.
• Experience in predictive analytics, data analysis, and model validation.
• Proficiency in scripting languages like Python or R.
• Strong SQL and data preprocessing skills.
• Knowledge of regulatory considerations in modeling.

Benefits & Perks

• Comprehensive medical, dental, and vision plans.
• 401(k), pension, and life insurance.
• Parental benefits and adoption assistance.
• Paid time off, holidays, and 16 paid volunteer hours.
• Wellness programs and professional development support.