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The AI Product Manager turns banker needs into product — translating real community\-banking workflows into the semantic and metric model, owning the accuracy standard that gates every release, and running design\-partner relationships. Working under the VP of Product Management and in close collaboration with engineering, design, advisory, and client success teams, this role keeps Navanta’s AI products anchored to what bankers actually need and to a provable standard of accuracy — driving real\-workflow products, design partners that convert into paying customers, and sustainable growth across Navanta’s AI solution set.
Key Responsibilities
- Run discovery with design\-partner banks and translate real workflows into the semantic and metric model and the product roadmap
- Define and maintain the golden\-question set — the verified questions and answers that gate every release
- Prioritize early delivery, managing trade\-offs between speed, accuracy, and security in an on\-premises, regulated\-institution context
- Own the AI product narrative for bankers and for procurement and security stakeholders
- Partner with design and engineering and facilitate cross\-functional teams on a banker\-friendly experience, scope and sequencing, and product enablement, including piloting with clients
- Manage the product lifecycle for AI\-enabled banking solutions — systems integration, analytics, roadmap planning, and product collateral and positioning for new and existing products
- Research market trends and conduct competitive analysis to inform product positioning, objectives, and roadmap priorities
- Partner with vendors and third\-party strategic partners to support new and existing product initiatives
- Define success metrics and instrument product adoption
Core Competencies
- Semantic and metric modeling, with data\-informed decision\-making
- Defining acceptance criteria and evaluation requirements for AI accuracy
- Executive communication and stakeholder management across bankers, procurement, and security
- Results orientation and accountability for product outcomes
Key Performance Indicators (KPIs)
- Golden\-question accuracy maintained or improved release over release
- Product adoption and active usage across design\-partner and production banks
- On\-time delivery against roadmap milestones and release gates
- Design\-partner satisfaction and reference\-ability
- Conversion of design partners into paying customers
Qualifications
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.
- 7–12\+ years in product management, including enterprise or B2B software shipped to demanding customers in regulated industries
- Experience with data or analytics products (metrics, semantic modeling), and ideally AI/ML features
- Demonstrated success with design\-partner and early\-customer motions and security\-gated enterprise sales cycles
- Strong written communication and stakeholder management
Core Technologies
- Domain: deposits and banking metrics (or a fast, credible ramp); SQL literacy a plus
- Practices: customer discovery, semantic and metric modeling, and acceptance criteria for AI accuracy
- Tools: modern product\-analytics and collaboration tooling
Nice to Have
- Background as (or close to) a banker, or prior fintech or community\-banking product work
- Experience specifying evaluation requirements for AI systems
Education and/or Experience
- Bachelor’s degree in business administration, marketing, or communication, or 3 to 5 years of equivalent experience in a product\-related role
- Experience in the financial industry or a technology\-focused environment required
Work Structure \& Expectations
- Full\-time role focused on both ongoing product delivery and milestone\-based initiatives that support growth and client impact
- Close collaboration with leadership and cross\-functional teams across engineering, design, advisory, and client success
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit and use hands to finger, handle, or touch objects, tools, or controls. The employee frequently is required to talk or hear. The employee is occasionally required to stand; walk; and stoop, kneel, crouch, or crawl. The employee must occasionally lift and/or move up to 10 pounds, usually waist high, up to 50 feet away. Specific vision abilities required by this job include close vision and the ability to adjust focus.
Work Environment
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Typical office environment
- Up to 20% travel time may be required
Who is Navanta?
Navanta is the trusted technology and services partner for community financial institutions, unifying critical systems, security, cloud infrastructure, and support into one seamless, purpose built experience. With more than 35 years of banking expertise — from Managed IT to Core Banking, CRM, and Advisory Services — Navanta helps institutions simplify complexity, reduce risk, and strengthen daily operations. Navanta empowers community bankers and their people to thrive together. Go Bankers, Go.™
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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Navanta, 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 $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Navanta AI Hiring
Navanta has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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