AI Technical Product Lead

Job Description

Publicis Sapient is looking for an Associate Director-level AI Product Manager to join our Product Management craft, focused on renewable energy and solar. This role sits at the intersection of clean-energy business strategy, artificial intelligence, data, and engineering. The AI Product Manager will identify and prioritize high-value AI opportunities across the solar and renewable energy value chain, translate business and user needs into a clear product vision and roadmap, and lead multidisciplinary teams from discovery and experimentation through scaled adoption. The ability to speak credibly with renewable energy leaders, data scientists, engineers, operators, and client stakeholders is essential.

Your Impact

Partner with solar and renewable energy clients, product leaders, operators, data scientists, machine learning engineers, software engineers, architects, designers, and strategists to identify business problems that can be solved responsibly with AI.
Own the AI product vision, strategy, roadmap, and prioritized backlog for solutions supporting areas such as solar asset performance, generation forecasting, predictive maintenance, anomaly detection, energy trading and optimization, customer acquisition, project development, field operations, and grid integration.
Lead discovery across utility-scale, commercial and industrial, and distributed solar workflows, converting customer and operational needs into product hypotheses, epics, features, user stories, acceptance criteria, data requirements, model requirements, and non-functional requirements.
Evaluate where machine learning, generative AI, computer vision, forecasting, optimization, and intelligent automation can create measurable value, while distinguishing true AI opportunities from problems better solved through workflow, analytics, or platform improvements.
Collaborate directly with data science and engineering teams to define model objectives, training and inference data needs, evaluation metrics, human-in-the-loop workflows, integrations, APIs, cloud architecture, monitoring, and production-readiness requirements.
Drive AI product experimentation from problem framing and prototype development through pilot, validation, deployment, adoption, and continuous improvement.
Translate complex AI concepts, model outputs, uncertainty, limitations, and technical trade-offs into clear business decisions for senior client and operational stakeholders.
Define measurable product and model outcomes, including business value, user adoption, forecast accuracy, reliability, operational efficiency, model performance, safety, and responsible AI controls.
Build data and feedback strategies that improve product performance over time, including data quality, labeling, lineage, access, governance, model monitoring, drift detection, and retraining considerations.
Embed responsible AI and product risk management throughout the lifecycle, addressing explainability, privacy, security, bias, reliability, regulatory considerations, and appropriate human oversight.
Facilitate product discovery, roadmap, technical design, model review, and stakeholder decision sessions across product, data, engineering, architecture, UX, operations, commercial, and client teams.
Build trusted relationships with senior renewable energy stakeholders and influence investment, prioritization, and adoption decisions without formal authority.

Qualifications

Experience operating at a senior Product Manager, AI Product Manager, Product Owner, or similar product leadership level in complex enterprise environments.
Demonstrated experience in renewable energy, solar, energy transition, utilities, or adjacent clean-energy markets, with an understanding of industry business models, operational workflows, data, assets, and technology ecosystems.
Proven experience taking AI, machine learning, advanced analytics, or data products from discovery and experimentation into production and measurable business adoption.
Strong AI and technical fluency. You do not need to be a data scientist or software engineer, but you must be comfortable discussing machine learning and generative AI concepts, model lifecycle, data pipelines, cloud services, APIs, integrations, system architecture, MLOps, evaluation, monitoring, and technical dependencies.
Ability to work with data scientists and engineers to define model and product requirements, challenge solution designs, ask meaningful technical questions, and translate technical implications into business impact and decision points.
Understanding of AI product evaluation, including the use of offline and online metrics, benchmarks, experimentation, user feedback, operational KPIs, and business outcomes.
Working knowledge of responsible AI principles, data governance, privacy, security, explainability, model risk, and human-in-the-loop design.
Hands-on experience developing product strategies, roadmaps, business cases, requirements, user stories, acceptance criteria, process flows, and launch or adoption plans.
Experience working across multidisciplinary teams that may include renewable energy subject matter experts, operations, engineering, architecture, data science, data engineering, QA, UX/design, legal, security, and business stakeholders.
Proven ability to prioritize competing demands, manage ambiguity, facilitate decisions, and secure stakeholder alignment in a fast-evolving technology environment.
Executive-level communication and storytelling skills, with the ability to communicate credibly with senior business leaders and deeply technical teams.
Comfort working with financials, KPIs, product analytics, and value realization measures to guide prioritization and investment decisions.

Set Yourself Apart With

Experience with solar development, photovoltaic asset management, energy forecasting, distributed energy resources, battery energy storage, virtual power plants, energy markets, grid operations,