AI Engineer / Data Scientist, AI Sr. Manager

PwC•Washington, DC

2d•$119,000 - $337,000•Onsite

About The Position

As part of the AI Engineering team within the Digital Assurance & Technology team, you will play a pivotal role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Within our Assurance practice, you will focus on developing and implementing 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 set direction and quality standards across work streams and teams for testing and evaluation of AI-enabled systems, technical evidence and control effectiveness, and reusable capabilities that support responsible, reliable AI deployment across multiple teams. As a Senior Manager, you will leverage your skills and professional network to deliver quality results. You will motivate and coach others, collaborating to solve complex problems. With increased autonomy, you will apply sound judgment, recognizing when to take action and when to escalate. Your ability to develop and sustain diverse and inclusive teams, along with your commitment to excellence, contributes to the success of our firm. In this role, you will lead large projects and innovate processes, focusing on achieving results and maintaining operational excellence. You will interact with clients at a strategic level, driving overall project success. Your influence and skills will be crucial in crafting clear, impactful messages and applying systems thinking to identify underlying problems and opportunities.

Requirements

At least a Bachelor's degree
At least 6 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
Demonstrating advanced skills in AI implementation and complex data analysis
Utilizing programming languages and data modeling for innovative solutions
Excelling in managing data infrastructure and integration projects
Applying deep learning and machine learning techniques effectively
Leading teams through complex data-driven projects with strategic advising
Leading cross-functional teams responsible for production AI deployment, monitoring, evaluation, and incident response
Establishing rigorous evaluation standards covering failure modes, bias, drift, hallucinations, explainability, and guardrails
Guiding architecture trade-offs and developing methods to measure and communicate technical and business outcomes across AI initiatives
Translating technical findings, limitations, and architecture trade-offs for executive clients and non-technical stakeholders
Developing technical leaders and managers across teams

Responsibilities

Leading the design and development of advanced AI and machine learning solutions to drive innovation and enhance business processes
Transforming raw data into actionable insights to support informed decision-making and business growth
Designing and optimizing algorithms, models, and systems for intelligent decision-making and automation
Managing complex data analysis and integration to improve data quality and infrastructure
Implementing data modeling techniques to support robust data solutions for clients
Collaborating with teams to validate outcomes with clients and incorporate feedback into project development
Directing teams through complex challenges, maintaining composure in ambiguous situations
Initiating coaching conversations to develop and sustain high-performing, diverse, and inclusive teams
Setting strategy and quality standards for evaluating AI system behavior, performance, robustness, bias, explainability, and failure modes.
Establishing standards for reviewing technical controls and evidence, and documenting defensible findings, limitations, and recommendations.
Directing the development of reusable evaluation tools, automated testing, reference datasets, monitoring capabilities, and evidence workflows across teams.