Manager, Data Science (Non-Financial Risk)

KPMG•Mclean, VA

48d

About The Position

KPMG Advisory practice is currently our fastest growing practice. We are seeing tremendous client demand, and looking forward we do not anticipate that slowing down. In this ever-changing market environment, our professionals must be adaptable and thrive in a collaborative, team-driven culture. At KPMG, our people are our number one priority. With a wealth of learning and career development opportunities, a world-class training facility and leading market tools, we make sure our people continue to grow both professionally and personally. If you're looking for a firm with a strong team connection where you can be your whole self, have an impact, advance your skills, deepen your experiences, and have the flexibility and access to constantly find new areas of inspiration and expand your capabilities, then consider a career in Advisory. KPMG is currently seeking a Manager, Data Science in Non-Financial Risk for our Consulting practice.

Requirements

Minimum six years of recent professional experience working in advanced analytics and data science; minimum two years of recent experience managing teams and delivering complex and critical projects
Bachelor's degree from an accredited college/university in a relevant STEM field such as data science, computer science, engineering, mathematics, physics and other related fields
Extensive experience in AI/ML algorithm development and data analysis including at least one of the following: NLP, time-series analysis, predictive modeling; experience with scripting, data structures and algorithms and ability to work with large amounts of data
Experience in a statistical programming language (for example, R or Python) and related data science / machine learning packages (for example, Pandas, Scikit-learn, Pytorch, Transformers)
Excellent communication, written, presentation, and problem-solving skills
Previous technical client service experience preferred
Ability to travel as required (based on location and clients served)
Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future; KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa)

Responsibilities

Serve as the technical lead on projects to design and develop advanced AI/ML solutions to meet clients' unique requirements, including participation in internal and external discussions to gather business use case requirements, provide advanced analytics and data science expertise and solution options for business problems
Engineer solutions using natural language processing and machine learning techniques to solve critical problems and improve processes for clients across capital markets and financial services businesses, including trade surveillance, electronic communications surveillance, payments fraud detection, third-party risk management and other operational risk categories
Utilize machine learning, natural language, and statistical analysis methods, such as sentiment analysis, topic modeling, time-series analysis, regression, classification, statistical inference, and validation methods to review financial services client risks
Perform explanatory data analyses, generate and test working hypotheses; prepare and analyze historical data and identify patterns to develop innovative solutions to financial services operational risk and regulatory compliance programs
Lead technical teams, mentor junior data scientists, and grow data science expertise within the broader team, including offshore; collaborate with diverse, cross-functional teams to accurately identify and prioritize requirements, ensuring that AI/ML solutions meet the needs and expectations of various stakeholders
Present to key stakeholders, such as approach, data requirements, interim findings, and final solution architecture and infrastructure
Act with integrity, professionalism, and personal responsibility to uphold KPMG's respectful and courteous work environment

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