Data Scientist Lead - Compliance Analytics

Manages and builds systems of models to analyze diverse big data sources to generate insights and solutions for business partners and product enhancement. Manages and participates in developing, testing and validating models that drive business value. Assists with identifying and interpreting insights from data.  Direct leadership of assigned data science team.

• Manage and participate in working with large data sets to solve unstructured problems using different analytical and statistical approaches for a single domain.
• Manage and participate in sourcing, ingesting, and cleaning of data sets in preparation for analysis, work with data teams to productionize and scale data cleanup process. Ensure data is stable, accounting for complex data drift in development and production.
• Manage and participate in the building of econometric, statistical and machine learning models for various problems inclusive of classification, clustering, pattern analysis, sampling, and simulations.
• Manage committing of complex coding into model repository to serve as a source for others and promote complex models into production system.
• Manage and develop champion/challenger models and adjust models accordingly.
• Manage, develop and implement framework for building self-healing models.
• Lead the selection and refinement of models taking into account performance, reliability and stability metrics and business feedback. 
• Develop model refinement educational materials and deliver related training for data users.
• Guide less experienced data scientists on model development, selection, refinement, measurements, and visualizations and creating consumable model outputs.
• Create outputs from multiple models for business discussions to display model outcomes, impact and business value.
• Lead stakeholder meetings to discuss concerns, opportunities and production challenges.
• Work with more experienced data scientists to develop new research approaches, provide recommendations on how techniques will be adapted based on client needs, and attend meetings with data clients to understand their research questions. 
• Review own and assigned team’s code to ensure it is efficient, accurate, and using best practices.
• Exercise usual authority of a manager concerning staffing, performance appraisals, promotions, salary recommendations, performance management and terminations.
• Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite.  Design, implement, maintain and enhance internal controls to mitigate risk on an ongoing basis.  Identify risk-related issues needing escalation to management.
• Promote an environment that supports belonging and reflects the M&T Bank brand.
• Maintain M&T internal control standards, including timely implementation of internal and external audit points together with any issues raised by external regulators as applicable.
• Complete other related duties as assigned.

SPECIFIC TO POSTING:

• Oversee the development of complex analysis and judgment-based work to support the identification and quantification of compliance risk.
• Utilize a data driven approach to assess the effectiveness of risk controls, identifying exceptions and investigating root cause.
• Produce efficient, automated self-service solutions that empower non-technical stakeholders to derive data-driven insights and conclusions
• Work with business units and compliance groups to ensure consistent understanding of requirements.
• Execute independent assignments within defined timelines with attention to detail and an investigative, quality-first mindset

Number of Staff: 6-8

• Bachelor’s degree and a minimum of 7 years related experience, or in lieu of a degree, a combined minimum of 11 years higher education and/ or work experience, including a minimum of 7 years related experience
• Minimum of 2 years managerial, supervisory and/or work leadership experience
• Intermediate experience working with multiple statistics and following data science principles such as AB testing, sample selection, hypothesis testing, and modeling bias
• Intermediate proficiency with pertinent statistical software and languages and tools
• Experience with various hybrid databases both on premise and in the cloud
• Intermediate level knowledge of Structured Query Language (SQL) and Not Only Structured Query Language (nSQL)
• Expert understanding of modeling techniques such as Bayesian modeling, Classification models, Cluster analysis, Neural Network, Non-parametric methods, and Multivariate statistics
• Experience analyzing large data sets

IDEAL QUALIFICATIONS PREFERRED:

• Masters’ of Science or Doctorate degree in Statistics, Economics, Finance or related field in the quantitative social, physical or engineering sciences, with proven coursework proficiency in statistics, econometrics, economics, computer science, finance or risk management
• Fluent in econometric/statistical techniques, including time-series analysis, panel data methods and logistic regression
• Tactical experience with pertinent statistical software and languages and tools