Data Scientist - Anti-Money Laundering Analytics & Modeling

PNC Bank•Pittsburgh, PA

12h•Onsite

About The Position

As a Data Scientist 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.

Requirements

Master's degree or higher in a quantitative field
Experience with data mining and data preparation for ML models
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, and/or SQL
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
Ability to manage multiple projects simultaneously
Strong teamwork skills and ability to work across different departments
Successful candidates must demonstrate appropriate knowledge, skills, and abilities for a role.
Analytical Thinking
Competitive Advantages
Data Analytics
Data Mining
Data Science
Machine Learning (ML)
Data Architecture
Data Mining
Disruptive Innovation
Information Capture
Machine Learning
Modeling: Data, Process, Events, Objects
Prototyping
Query and Database Access Tools
Roles at this level typically require a university / college degree, with 3+ years of relevant / direct industry experience.
In lieu of a degree, a comparable combination of education, job specific certification(s), and experience (including military service) may be considered.

Nice To Haves

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
Experience with cloud platforms like AWS, Google Cloud, or Azure

Responsibilities

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
Performs analytical tasks on vast amounts of structured and unstructured data to extract actionable business insights.
Participates in the data gathering, data processing and data mining of large and complex datasets.
Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
Assists in the presentation of business insights to management using visualization technologies and data storytelling.
May partner with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.