Risk Modeling Services - AI Data Scientist Director

PricewaterhouseCoopers•San Francisco, NY

2d•$134,000 - $348,500

About The Position

The Risk Modeling Services - AI Data Scientist Director leads the development and delivery of AI-enabled modeling solutions for client needs within PwC’s Assurance practice. The Director sets strategic direction, shapes executive-level client relationships, and guides teams in developing ideas, translating vision into action, and identifying opportunities that advance the firm’s business goals. The role oversees multiple workstreams focused on software and product innovation, collaborates across technology delivery teams, and helps build solutions combining machine learning, data science, and practical business insight. It also involves leading client discussions, overseeing data science solution delivery from design through deployment, and guiding teams across technical problem solving and project delivery. Travel may be required up to 60%. The salary range is $134,000–$348,500, or $134,000–$410,000 for Washington state residents. Actual compensation depends on skills, experience, qualifications, location, and applicable employment laws. Hired individuals are eligible for an annual discretionary bonus.

Requirements

At least a Bachelor's degree.
At least 9 years of experience.
Adhere to professional standards, code of conduct, and independence requirements.

Nice To Haves

Drive AI data science initiatives for risk modeling engagements.
Lead strategic client conversations on AI-enabled risk solutions.
Develop thought leadership in applied machine learning.
Translate complex technical ideas into business recommendations.
Mentor teams across multiple projects and delivery streams.

Responsibilities

Set strategic direction for AI-driven risk modeling and technology delivery engagements.
Lead client discussions to identify business needs and translate them into data science solutions.
Develop and oversee machine learning models, predictive analytics methods, and model governance practices.
Guide end-to-end delivery of data science solutions across design, build, testing, and deployment phases.
Evaluate data sources, feature engineering approaches, and model performance to improve accuracy and reliability.
Drive innovation in AI, automation, and digital tooling to advance client service delivery.
Collaborate across technology, risk, and business teams to align solution design with delivery objectives.
Coach teams on technical problem solving, project planning, and release management activities.
Apply AI fluency, analytical thinking, and software development knowledge to complex delivery challenges.
Identify market opportunities and shape offerings that support growth across risk modeling services.
Shape client relationships at the executive level and guide teams across multiple software and product innovation workstreams.