AI & Machine Learning Engineering Consultant - Power & Utilities Sector - Manager - Consulting

EY•Hartford, DC

3d•Hybrid

About The Position

Our Artificial Intelligence and Data team helps apply cutting-edge technology and techniques to bring solutions to our clients. As part of that, you'll sit side-by-side with clients and diverse teams from EY to create a well-rounded approach to advising and solving challenging problems, some of which have not been solved before. No two days will be the same, and with constant research and development, you'll find yourself building knowledge that can be applied across a wide range of projects now, and in the future. You'll need to have a passion for continuous learning, stay ahead of the trends, and influence new ways of working so you can position solutions in the most relevant and innovative way for our clients. You can expect heavy client interaction in a fast-paced environment and the opportunity to develop your own career path for your unique skills and ambitions. In this role, you will research, build, and implement scalable artificial intelligence systems that learn and make predictions tailored to business requirements across various environments, including cloud and on premises. You will enhance data pipelines and storage, ensuring data is clean, accurate, and optimized for XOps processes. Additionally, you will monitor and evaluate learning processes to continuously improve high-performance models, collaborating with other data and analytics professionals to industrialize analysis into effective analytics solutions. Within Power & Utilities engagements, these solutions will commonly support regulated environments such as electric, gas, and water utilities, addressing domains including grid and asset reliability, outage prediction and response, capital planning, customer operations, regulatory reporting, and operational efficiency across generation, transmission, distribution, and field operations.

Requirements

A Bachelor’s degree required (4-year degree).
6-10 years of relevant experience of full-time working experience in AI, Data Science, and/or Machine Learning
2-4 years of experience directly managing technical teams
Strong skills in Python
Ability lead, collaborate, and communicate effectively with diverse, hybrid and global teams
Experience designing, building, and maintaining high-impact, high-value production AI/ML solutions on a major cloud platform
Proficient in Generative AI models and frameworks (e.g., OpenAI, Dall-e, Langchain, Retrieval Augmented Generation (RAG)) and experienced with ML packages like scikit-learn and PyTorch
Experience with natural language processing and deep learning
Extensive experience in DevOps tools (GIT, Azure DevOps), Agile methodologies (Jira), and CI/CD pipelines for developing, deploying, and scaling analytical solutions
Experience with MLOps and ML workflows, including data ingestion, transformation, and evaluation
Experience with model retraining and feedback loop methodologies
Experience with model and solution monitoring and reporting
Understanding of data structures, data modelling and software engineering best practices
Strong foundation in mathematics, statistics, and operations research, with proficiency in data manipulation tools (SQL, Pandas, Spark) and deep learning techniques
Excellent communication skills for conveying findings and recommendations, with a willingness to travel for client engagements
Skills in Technical Design Optimization
Strong relationship-building skills
Demonstrated client trust and value
Digital fluency and emotional agility
Commercial acumen and negotiation skills
Proven ability to lead teams and manage change
Experience delivering analytics or AI solutions in asset-intensive environments (e.g., utilities, energy, infrastructure, manufacturing, or transportation) is strongly preferred.
Familiarity with utility regulatory, compliance, or operational data considerations is a plus.

Nice To Haves

A deep understanding of and ability to teach concepts, tools, features, functions, and benefits of different approaches to apply them
Master's degree Computer Science, Mathematics, Physical Sciences, or other quantitative field
Experience working with diverse teams to deliver complex solutions
Strong skills in languages beyond Python: R, JavaScript, Java, C++, C
Experience fine-tuning Generative AI models
Experience in managing complex projects with multiple stakeholders
A strong understanding of industry trends and emerging technologies
Skills in data visualization and storytelling with data
Experience with image processing techniques and/or speech and audio processing and analysis
Exposure to Power & Utilities transformation programs, such as grid modernization, advanced metering, wildfire mitigation, energy transition, DER integration, or digital operations initiatives.
Experience supporting executive-level decision-making in regulated environments, including preparation of materials that may be reviewed by regulators or governing bodies.

Responsibilities

Leading workstream delivery and ensuring the effective management of processes and projects.
Continuously improving processes by identifying innovative solutions through research and analysis.
Managing professional employees and supervising teams to deliver complex technical initiatives, with accountability for performance and results.
Engaging actively with clients, participating in daily working sessions, and leading workstreams from planning through execution to closure.
Identifying opportunities for additional services and managing engagement economics.
Designing and delivering AI/ML use cases relevant to Power & Utilities, such as asset health and failure prediction, outage detection and restoration optimization, vegetation management analytics, demand forecasting, load and DER forecasting, predictive maintenance, customer operations optimization, and regulatory analytics.
Working with utility data sources including SCADA, AMI/AMI 2.0, GIS, EAM (e.g., Maximo), OMS, CIS, and historian systems, and integrating these into modern analytics platforms.
Supporting utilities in moving AI solutions from pilots to production while meeting regulatory, audit, cybersecurity, and data governance requirements.