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About This Role
Adventure is our Culture. Join a team that celebrates a lifestyle as bold as the terrain we love. At Backcountry, we are rooted in adventure, recognition, and wellbeing on and off the mountain. We spotlight employee stories, celebrate milestones, and offer exclusive outdoor perks. Whether you are at HQ, in a retail store, or remote, you will be part of a team that thrives on energy, exploration, and connection.
Reports to: Vice President of Product and UX
Location: Cottonwood Heights, Utah (Hybrid - 3x/week in-office)
About the Role
Backcountry is moving from manual, low-volume A/B testing to an always-learning agentic platform that designs, launches, monitors, and optimizes experiments across our family of ecommerce sites. As Senior Product Manager, AI Experimentation Platform, you will own this platform from the ground up — setting the roadmap, defining where AI agents act autonomously and where humans stay in the loop, and driving measurable gains in conversion and revenue.
At six to twelve months, success looks like a working platform that runs trusted experiments across multiple sites, a significant increase in testing velocity without sacrificing statistical rigor, and revenue and conversion improvements you can trace directly to platform-run experiments.
This is a lean team. You will own a lot, move fast, and make decisions with full end-to-end responsibility.
What You'll Do
- Own the vision, roadmap, and prioritization for the agentic experimentation platform, deciding what gets built first, what waits, and what to buy versus build.
- Define how AI agents participate across the experiment lifecycle — hypothesis generation, variant creation, test setup and QA, live monitoring, and ship-or-rollback decisions.
- Set guardrails that keep autonomous testing safe: statistical standards, sample ratio mismatch and anomaly checks, revenue and margin thresholds, approval workflows, and automatic rollback.
- Build adaptive optimization into the platform using multi-armed and contextual bandits so traffic shifts toward winning experiences while tests are still running.
- Partner with engineering and data science on core infrastructure: assignment and bucketing, feature flags, event tracking, the metrics layer, and the stats engine built on GA4 and BigQuery.
- Define and instrument platform success metrics — test velocity, win rate, revenue impact, and result trustworthiness — from day one.
- Make experimentation self-serve for merchandising, marketing, UX, and engineering teams so they can launch and read out tests without a specialist.
- Create standards and playbooks for metric definitions, test design, and readouts so results are consistent, comparable, and trusted across every site.
- Partner across engineering, design, analytics, merchandising, and marketing to turn business goals into a shipped platform and a culture of testing.
Required Qualifications
- 5+ years of product management experience, including ownership of a platform, data, or internal tooling product.
- Hands-on experience running or building A/B testing and experimentation programs at scale in ecommerce or other high-traffic digital products.
- Strong statistical grounding in experiment design: power and sample size calculation, significance, peeking, multiple comparisons, and guardrail metrics.
- Hands-on experience building with AI and LLMs, including agentic workflows, prompt design, tool integrations, and evaluating AI output quality.
- Working knowledge of experimentation infrastructure — feature flagging, randomization, event tracking, and warehouse-native metrics.
- Strong analytical skills with hands-on use of SQL, GA4, BigQuery, Looker, or similar tools.
- Demonstrated impact on conversion, revenue, or testing speed with measurable results.
- Excellent communication skills and the ability to influence engineering, data, and business stakeholders.
Preferred Qualifications
- Experience with experimentation platforms such as Optimizely, Statsig, Eppo, GrowthBook, LaunchDarkly, or an in-house equivalent.
- Experience with adaptive experimentation — multi-armed or contextual bandits, Bayesian methods, or variance reduction techniques like CUPED.
- Experience testing AI or ML-driven experiences such as search, recommendations, or personalization.
- Experience running experimentation across multiple brands or storefronts on a shared platform.
- Background in outdoor, specialty, or multi-brand ecommerce.
Why Join
The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
- Cross-Functional Impact: Build the system that determines how every one of our sites improves, with results that show up directly in revenue and conversion across the portfolio.
- AI-First Skill Building: Work on a team that treats AI as a real upgrade to quality and speed, and build a product where AI agents do real work in production — from hypothesis generation to autonomous optimization.
- End-to-End Ownership: Define what an agentic experimentation platform looks like here, from the first architecture decisions to the first autonomous test, with full ownership of the roadmap and outcomes.
- Competitive Benefits: Paid time off policies, 401(k) match, medical/dental/vision and a variety of supplemental policies, and employee discounts across our portfolio of brands.
* Recruiter Screen - A 30-minute conversation with our recruiting team to align on the role, your background, and what you are looking for.
- Take-Home Challenge - A short AI or product building exercise so we can see how you approach problems and execution.
- Hiring Manager Interview - A deep dive with the Vice President of Product and UX on your experience, product mindset, and approach to experimentation.
- Technical and Case Discussion - A working session with engineering and analytics on experiment design, statistics, and platform architecture.
- In-Person Interview - We'd like to meet you at our Cottonwood Heights office. Details and logistics will be arranged with your recruiter.
- Reference Checks - Conducted in parallel with the final stages where possible.
- Offer - We move quickly for the right candidate.
Interview process is subject to change. Any updates will be communicated promptly and clearly.
- Recruiter Screen - A 30-minute conversation with our recruiting team to align on the role, your background, and what you are looking for.
- Take-Home Challenge - A short AI or product building exercise so we can see how you approach problems and execution.
- Hiring Manager Interview - A deep dive with the Vice President of Product and UX on your experience, product mindset, and approach to experimentation.
- Technical and Case Discussion - A working session with engineering and analytics on experiment design, statistics, and platform architecture.
- In-Person Interview - We'd like to meet you at our Cottonwood Heights office. Details and logistics will be arranged with your recruiter.
- Reference Checks - Conducted in parallel with the final stages where possible.
- Offer - We move quickly for the right candidate.
Interview process is subject to change. Any updates will be communicated promptly and clearly.
Please Note
- Part of our interview process is a mandatory in-person interview with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately.
- Due to a high volume of fraudulent applications, you must share a valid LinkedIn profile URL in the application questions below to be considered. If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.
CSC Generation is an equal opportunity employer. All qualified applicants will receive consideration without regard to any characteristic protected by law. If you need an accommodation during the application process, contact [email protected].
Employment is contingent on a background check, including verification of prior employment dates.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 11,520 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At CSC Generation, 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 Required
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 $220,200 based on 993 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CSC Generation AI Hiring
CSC Generation has 1 open AI role right now. They're hiring across AI Product Manager. Based in Cottonwood Heights, UT, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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