Position Overview

You can play a critical role in the success of PNC as a member of our Retail Bank Operations team. You’ll help drive crucial behind-the-scenes functions for many lines of business. This includes essential items such as managing the cash needs of our branch and ATM networks, protecting the bank from potential fraud, and identifying ways to continually improve our processes. If you’re ready for exciting new challenges in your career, bring your passion and expertise to PNC.

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 within PNC's Retail Bank Operations organization, you will be based in Cleveland, Columbus OH; Pittsburgh, PA; Laredo, McAllen, Houston, Harlingen, TX; Birmingham, AL; Tampa, FL or Phoenix, AZ.

This is a remote position. Work may be performed from a quiet, confidential space in a home location, approved by PNC. This position may not be available in all geographic locations.

Additional locations within the PNC Footprint may be considered.

As a Data Scientist on the Bank Operations Analytics(BOA) Team in Retail Operations, you will leverage your data science experience and banking knowledge to develop predictive models that contribute to PNC's data science strategy and enhance the Retail customer experience.

You will partner with:
• Retail Banking LOBs to understand business processes and identify opportunities for: improved efficiencies, financial savings, risk reduction, and an enhanced customer experience
• Data Experts, Data Architects, and Data Engineers for data mining and processing, structured and unstructured data, in order to extract business insights
• Model Risk Management to assess model risk, governance, and monitoring
• MIS to deploy models
• Other Bank Operations Analytics members to knowledge share and assist in the development of data science and machine learning skills within the team

Preferred Competencies/Skills:
• Demonstrated history of developing algorithms that use advanced mathematical and statistical techniques, like machine learning, to predict business outcomes and recommend optimal actions to management
• Familiar with how data science can be applied in Cash Management, Fraud, Disputes, and other aspects of Banking
• Experience building machine learning algorithms, preferably in a financial institution
• Experience with Supervised and Unsupervised learning models including: Classification, Regression, Forecasting, and Clustering
• Experience with data mining and data processing of large, complex, structured, and unstructured data
• Experience with Cloudera Hive & Impala
• Knowledge of tools/languages for data mining, data wrangling, feature engineering, and model development including: SQL, Spark, Python, R, PySpark, Pandas, Koalas, etc.
• Can confidently present analytical insights to technical and business audiences
• Prototyping: Knowledge of and ability to implement prototyping disciplines, tools and techniques in evolutionary models within the target environment
• Disruptive Innovation: Knowledge of concepts, principles, and approaches of disruptive innovation; ability to adopt the knowledge into related processes and practices

Job Description

• 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