Data Scientist Senior - Compliance Analytics

Develops complex methods, processes, and systems to consolidate and analyze diverse big data sources to generate insights and solutions for business partners and product enhancement. Works with more experienced data scientists to develop new research approaches.

POSITION RESPONSIBILITIES:

Work with large and complex data sets to solve unstructured problems using different analytical and statistical approaches for multiple products independently.

Lead sourcing, ingesting, and cleaning of data sets in preparation for analysis, and assist more experienced data scientists to productionize and scale data cleanup process. Ensure data is stable, accounting for complex data drift in development and production.

Build complex econometric, statistical and machine learning models for various problems inclusive of classification, clustering, pattern analysis, sampling, and simulations.

Commit complex code into model repository and promote complex models into production system.

Develop champion/challenger models and adjust models accordingly.  Implement framework for building self-healing models.

Select and refine models, taking into account performance, reliability and stability metrics and business feedback.

Draft model refinement educational materials for data users.

Create model outputs for business discussions to display model outcomes, impact and business value. Lead less experienced team members in creating consumable model outputs. Attend stakeholder meetings to discuss concerns, opportunities and production challenges.

Work with more experienced data scientists to develop new research approaches and provide initial assessment of how techniques will be adapted based on client needs.

Review code to ensure it is efficient, accurate, and using best practices.

Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite.  Identify risk-related issues needing escalation to management.

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:

Perform 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

MANAGERIAL/SUPERVISORY RESPONSIBILITY:

None

MINIMUM QUALIFICATIONS REQUIRED:

Bachelor’s degree and a minimum of 5 years related experience, or in lieu of a degree, a combined minimum of 9 years higher education and/ or work experience, including a minimum of 5 years related experience

Experience working with multiple statistics and data science principles such as AB testing, sample selection, hypothesis testing, and modeling bias

Proficiency with pertinent statistical software and languages and tools (SAS, R, and Phyton)

Experience with various hybrid databases both on premise and in the cloud

Intermediate level knowledge of Structured Query Language (SQL)

Intermediate level knowledge of Power BI or any other visualization tool.

Intermediate 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

Fluent knowledge of data analysis tools, data visualization, developing analysis queries and procedures in Python, R, SQL, SAS, BI tools or other analysis software, and relevant industry data & methods and ability to connect external insights to business problems