AI Technical Product Lead

Publicis Groupe•Houston, TX

18h

About The Position

Publicis Sapient is seeking an Associate Director-level AI Product Manager to join its Product Management craft, focused on renewable energy and solar. The role sits at the intersection of clean-energy business strategy, artificial intelligence, data, and engineering. The AI Product Manager identifies and prioritizes high-value AI opportunities across the solar and renewable energy value chain, translates business and user needs into product vision and roadmaps, and leads multidisciplinary teams from discovery and experimentation through scaled adoption. The role requires credible engagement with renewable energy leaders, data scientists, engineers, operators, and client stakeholders. The position partners with solar and renewable energy clients and cross-functional teams to define and deliver AI products. It owns product strategy and prioritization, guides AI experimentation and production adoption, defines measurable product and model outcomes, and incorporates feedback, governance, and responsible AI risk controls throughout the product lifecycle. The role also builds relationships with senior stakeholders and influences investment, prioritization, and adoption decisions without formal authority.

Requirements

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, including 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, including comfort 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 using 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, potentially including 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.

Nice To Haves

Experience with solar development, photovoltaic asset management, energy forecasting, distributed energy resources, battery energy storage, virtual power plants, energy markets, grid operations, or renewable energy trading.
Experience developing AI use cases involving time-series forecasting, predictive maintenance, anomaly detection, optimization, computer vision, geospatial data, digital twins, or generative AI for renewable energy workflows.
Familiarity with renewable energy and solar data sources and platforms, such as SCADA, weather data, irradiance data, smart-meter data, asset performance management systems, GIS, market data, and energy management systems.
Experience with cloud AI/data ecosystems such as Microsoft Azure, AWS, or Google Cloud, and modern MLOps, analytics, and data-platform practices.
Prior consulting experience delivering AI-enabled or technology-enabled transformation for energy, utilities, or renewable energy clients.
AI product management, cloud, product, or Agile certification, such as Certified Scrum Product Owner (CSPO), SAFe Product Owner/Product Manager, or Micros...

Responsibilities

Partner with solar and renewable energy clients and multidisciplinary teams to identify business problems that can be addressed responsibly with AI.
Own the AI product vision, strategy, roadmap, and prioritized backlog for solutions across 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, translating needs into product hypotheses, epics, features, user stories, acceptance criteria, data requirements, model requirements, and non-functional requirements.
Evaluate potential uses of machine learning, generative AI, computer vision, forecasting, optimization, and intelligent automation, distinguishing AI opportunities from problems better addressed through workflow, analytics, or platform improvements.
Collaborate with data science and engineering teams to define model objectives, 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 prototyping through pilot, validation, deployment, adoption, and continuous improvement.
Explain AI concepts, model outputs, uncertainty, limitations, and technical trade-offs to senior client and operational stakeholders to support business decisions.
Define measurable product and model outcomes, including business value, adoption, forecast accuracy, reliability, operational efficiency, model performance, safety, and responsible AI controls.
Build data and feedback strategies covering data quality, labeling, lineage, access, governance, model monitoring, drift detection, and retraining considerations.
Embed responsible AI and product risk management across the lifecycle, addressing explainability, privacy, security, bias, reliability, regulatory considerations, and 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.