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ABOUT POWER FACTORS
Power Factors is a software and solutions provider leading the next generation of clean energy with Unity, one of the most extensive and widely deployed renewable energy management suites (REMS) in the market. With over 300 GW of wind, solar, and energy storage assets managed worldwide across more than 600 customers and 18,000 sites, Power Factors manages 25% of the world’s renewable energy data.\*
Power Factors’ Unity REMS supports the entire energy value chain, from monitoring and controls to market participation. The company’s suite of open, data\-driven applications empowers renewable energy stakeholders to collaborate, automate critical workflows, and make more informed decisions to maximize asset returns. Energy stakeholders receive end\-to\-end support, including solutions for SCADA \& PPC, centralized monitoring, performance management, commercial asset management, and field service management.
With deep domain expertise, AI\-powered insights are delivered at scale so businesses can optimize assets, unlock growth, and make smarter decisions as the world rapidly transitions to clean energy. Power Factors fights climate change with code.
- Outside China and India
ABOUT THE ROLE
This is a strategically essential Senior Product Manager role. You will own three connected areas of the Unity platform: the interface where users work, the business logic that makes their data trustworthy and powerful, and the insights that turn that data into action. Your work will shape how operators, analysts, and asset managers run hundreds of gigawatts of clean energy assets worldwide.
The UI, comprised of widgets, dashboards, reports, visualizations, actions, and workflows, is how users complete work in the platform and the primary way they interact with their data. Intuitive design and deep user empathy deliver powerful, efficient, and empowering capabilities.
Business Logic connects measured data to reporting. It covers data quality, KPI definitions, dependency management, traceability and transparency, and user\-facing configurability: everything that makes data trustworthy at scale.
Insights help customers identify, investigate, and act on performance signals. You will partner with data scientists to turn their research into real\-world impact.
User discovery and workflow mapping are at the center of the job. Ask good questions, form clear points of view quickly, and stay open to changing them. Success means complex workflows feel efficient and straightforward, with consistent standards and shared principles across all touch points. Concretely, the work looks like taking a workflow that today spans multiple screens, tools, and manual actions, and turning it into a single coherent flow, intentionally designing handoffs between humans, AI, and purpose\-built automations.
You will partner closely with User Experience Designers and Development Management as roles evolve and blend through AI tooling. Use AI tooling to handle routine mechanics so your energy goes to the strategic and human work, including rapid prototyping.
Collaboration with cross\-functional stakeholders is essential, working with Data Science, Sales, Customer Success, Support, and Product Marketing to capture input and maximize the user and business impact of what you build.
WHAT YOU WILL OWN
Product Strategy, Design System, and Workflow Foundations
- Own the product roadmap for core APM UI capabilities and the user workflows that tie them together.
- Define and prototype differentiated offerings using next\-gen UX and generative AI.
- Partner with UX designers to evolve and innovate on cohesive visual language and interaction patterns.
Business Logic and Advanced Insights
- Own the roadmap for product analytics, including Advanced Insights and APM generative AI products.
- Lead the functional areas APM users depend on: data quality, KPIs, dependency management, configurability, and UX interactions.
- Lead the productization of advanced analytics across descriptive, predictive, diagnostic, and prescriptive use cases, helping customers identify, investigate, and act on performance signals.
- Define the end\-to\-end insight journey: how results are surfaced, investigated, and resolved, with a focus on minimizing time\-to\-action and maximizing operational efficiency.
User Workflows and the AI\-Powered Paradigm Shift
- Own the end\-to\-end user workflows in the application: how operators, analysts, and asset managers actually do their jobs.
- Lead discovery and workflow mapping as a core practice: observe, interview, map, prototype, validate.
- Embed generative AI in the product to streamline workflow steps and free users to focus where it matters most. Maintain a clear view of where AI adds leverage versus where it introduces noise or unacceptable risk.
- Use modern AI tooling in your own workflow for prototypes, artifacts, and documentation.
Roadmap and Delivery
- Maintain a market\-validated roadmap grounded in customer discovery, competitive intelligence, and measurable outcomes.
- Write clear, executable PRDs and Epics. Use generative AI tooling actively to accelerate research, synthesis, and requirements generation.
- Own non\-functional requirements, with special emphasis on latency, performance, consistency, stability, and scalability; partner adeptly with platform and backend teams.
- Ensure shipped features land with complete documentation, enablement materials, and adoption instrumentation at launch.
- Track outcomes over outputs: instrument what you ship, watch adoption, run experiments, and iterate.
Cross\-Functional Leadership
- Partner closely with UX Design and Development Management as roles evolve and blend through AI tooling.
- Collaborate with Sales, Customer Success, Support, and Product Marketing to capture input and maximize the user and business impact of what you ship.
- Partner with the Data Science team to translate algorithmic capabilities into scalable, production\-grade product features.
- Engage directly with customers as a core input to learning and prioritization and translate what you hear into product strategy.
- Communicate clearly, in crisp writing, engaging presentations, and lively discussions, with technical and non\-technical audiences across Sales, Customer Success, Support, Product Marketing, customers, and executives.
WHAT YOU WILL NEED TO BE SUCCESSFUL
You thrive in a purpose\-driven environment where hard problems require both craft and strategic clarity. Specifically, we are looking for:
Product Craft, Data Fluency, and Workflow
- 5\+ years of product management experience, with meaningful time spent on UI, workflow, or platform\-capability products.
- Experience using and building data\-intensive or analytics products, with enough technical depth to engage data scientists and engineers on trade\-offs and design decisions.
- Experience bringing data science or machine learning products to market is a plus.
- Analytical problem\-solving paired with the creativity to craft applications customers enjoy using.
- Curiosity about the next generation of clean energy challenges, and dedication to building that future.
- Experience documenting business processes and improving them; clean energy experience is a plus.
- Strong design judgment and a partnership instinct with UX designers.
- Principled but pragmatic: you know when to hold a design line and when to compromise.
- Able to build trust, empathy, and real understanding with customer users. You form clear points of view quickly and hold them lightly.
Discovery, Metrics, and Iteration
- Hands\-on experience with experimentation and A/B testing, user research, and customer discovery as ongoing practices.
- Comfort with product analytics and usage data: you track what you ship and use that data to drive iteration priorities.
- Experience with KPI frameworks, data quality problems, or configuration\-heavy product areas is a meaningful differentiator.
AI Tooling and the Modern PM Practice
- Hands\-on use of generative AI tools in your day\-to\-day PM work: research, synthesis, requirements, and rapid prototyping.
- A habit of picking up new tools for rapid design and prototyping; you’re an early adopter and a tinkerer.
Communication \& Stakeholder Management
- Clear writing is essential: PRDs, epics, customer comms, internal narratives.
- Ability to make complex concepts accessible to non\-technical audiences.
- Comfortable demoing and defending product decisions in customer\-facing and executive settings.
- A track record of building alignment across UX, engineering, and commercial teams.
Domain Experience
- Not required, but a plus: Experience in clean energy, renewables operations, or adjacent industrial software domains. If you have lived it, you can help build the application you always wished you had.
- What matters more: curiosity, fast learning, willingness to dig into technical detail, and genuine passion for clean energy.
LIFE @ POWER FACTORS
We are an agile software development company – big enough to make an impact, but small enough to move quickly and execute in a growing industry, taking advantage of rapidly evolving technologies. We are a collective of bold and ingenious talents driven by results. Our team is made up of hard\-working, fun\-loving people who are passionate about making the world a better place. We seek fierce and humble people to help us achieve our ambitious plan.
WHY JOIN US
By joining the Power Factors team, you’ll be part of a dynamic group of innovative and driven individuals dedicated to making a positive impact. Every day, your work will directly contribute to advancing clean energy solutions and supporting global sustainability initiatives. Our culture runs deep and shows up in how we work together \- committed, conscientious and collaborative. With many opportunities for professional growth, Power Factors is here to support your development as we lead the charge in transforming the energy industry.
WE ARE AN EQUAL OPPORTUNITY EMPLOYER
Power Factors is an Equal Opportunity Employer committed to engaging a diverse workforce and sustaining an inclusive culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Power Factors, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Power Factors AI Hiring
Power Factors has 1 open AI role right now. They're hiring across AI Product Manager. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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