Position Overview

At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company’s success. As a Data Scientist Senior within PNC's Balance Sheet Analytics and Modeling AML Model Development organization, you will be based in Pittsburgh, PA, Birmingham, AL or Tysons, VA. This position is primarily based in a PNC location. Responsibilities require time in the office or in the field on a regular basis. Some responsibilities may be performed remotely at manager's discretion.

As a Data Scientist Senior within PNC's Anti-Money Laundering Analytics & Modeling team, you will be part of a cohesive team of professionals who utilize a variety of statistical techniques to build models to detect, monitor, and avert concerning patterns of account activity. In this role, you will work with key stakeholders across the bank to identify patterns and risk indicators within the firm’s account and transaction datasets, identify opportunities for new strategies, and recommend improvements to existing strategies. You will be leading innovative AML projects that are patentable, utilizing statistical techniques, including logistic regression, clustering, gradient boosting, neural network, and other machine learning algorithms, to design samples and build statistical models.

Specific responsibilities for this role include (but are not limited to):
• Use a variety of analytical techniques to extract usable information from various data sources, including customer, account, and transactional data sets
• Participate in data set creation, analysis, reporting, model building, model monitoring and model documentation
• Effectively communicate analytical results, represent the modeling team in various forums to inform senior executives and various team partners of progress on key modeling efforts
• Collaboration with 1st, 2nd and 3rd line of defenses and other key stakeholders

Preferred experience/qualifications:
• Master's degree or higher in a quantitative field
• Experience with data mining, and data preparation for ML models including EDA, data transformations and preprocessing
• Proficiency in statistical methods and tools, including experimental design, probability theory, and sampling
• Expertise in building, scaling, and optimizing machine learning systems with industry recognized ML frameworks and algorithms
• Strong programming skills in Python, PySpark, R, SAS, and/or SQL
• Familiarity with big data technologies like Hadoop, Spark, Hive, Impala etc.
• Experience working with model risk governing bodies in model validation, and with model implementation partners in productionizing a model
• Critical thinking and problem-solving aptitude with the ability to apply analytical rigor to complex business problems
• Ability to present complex technical concepts clearly and effectively to non-technical stakeholders and business partners
• Proven ability to lead and mentor other data scientists
• Ability to manage multiple projects simultaneously
• Strong teamwork skills and ability to work across different departments
Additional preferred qualifications:
• Master’s degree in Statistics or Econometrics
• Experience in banking/ financial services
• Experience with anti-fraud and/or anti-money laundering modeling
• Hands-on experience building various types of AI/ML models, including neural networks
• PhD in Statistics, Applied Statistics, or Econometrics
• Experience with cloud platforms like AWS, Google Cloud, or Azure

Job Description

• Leads the implementation of analytical projects that leverage vast amounts of structured and unstructured data to extract actionable business insights.
• Directs the data gathering, data processing and data mining of large and complex datasets.
• Leads the development of algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
• Leads analytical ex