AI Engineer / Data Scientist, AI Manager

PwC•Washington, DC

2d•$99,000 - $297,000

About The Position

As part of the AI Engineering team within the Digital Assurance & Technology team, you will be at the forefront of transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Within our Assurance practice, you will develop and implement advanced AI and machine learning solutions to drive innovation and enhance business processes. Your work will involve designing and optimizing algorithms, models, and systems to enable intelligent decision-making and automation. You will lead deliver of workstreams involving testing and evaluation of AI-enabled systems, review technical evidence and control effectiveness, and guide development of reusable tools that support responsible, reliable AI deployment. As a Manager, you will lead teams and manage client accounts, focusing on strategic planning and mentoring junior staff. You are accountable for confirming project success and maintaining standards. Enhancing your leadership style, you motivate, develop, and inspire others to deliver quality. You are responsible for coaching, leveraging team members' unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important role in identifying opportunities that contribute to the success of our firm. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same.

Requirements

At least a Bachelor's degree
At least 4 years of experience

Nice To Haves

Preference for at least one of the following fields of study: Analytics/Data Science, Artificial Intelligence/Robotics, Computer Science/Information Systems, Engineering
Excelling in AI implementation and complex data analysis
Utilizing Python and programming languages for data modeling
Developing data infrastructure and integration strategies
Implementing machine learning and deep learning techniques
Analyzing sentiment and pattern recognition for text analytics
Leading technical teams supporting production AI deployment, monitoring, evaluation, and incident response
Applying rigorous evaluation across failure modes, bias, drift, hallucinations, explainability, and guardrails
Making and communicating architecture trade-offs to deliver measurable, defensible outcomes
Measuring and communicating technical and business outcomes using defined methods
Translating technical findings, limitations, and architecture trade-offs for clients and non-technical stakeholders
Coaching team members while maintaining delivery quality

Responsibilities

Leading the development and implementation of advanced AI and machine learning solutions to drive innovation and enhance business processes
Designing and optimizing algorithms, models, and systems to enable intelligent decision-making and automation
Transforming raw data into actionable insights to support informed decision-making and drive business growth
Managing complex data analysis and integration to improve data quality and infrastructure
Overseeing data modeling and relational database management to support robust data solutions
Mentoring team members to leverage their unique strengths and manage performance to meet client expectations
Addressing conflicts or issues by engaging in difficult conversations with clients and stakeholders
Upholding professional and technical standards, including the firm's code of conduct and independence requirements
Encouraging the development of skills outside comfort zones and promoting teamwork and intellectual curiosity
Defining and reviewing testing approaches for AI system behavior, performance, robustness, bias, explainability, and failure modes
Reviewing technical controls and evidence to confirm findings, limitations, and recommendations are clear and defensible
Leading the development of reusable evaluation tools, automated tests, reference datasets, monitoring capabilities, and evidence workflows