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Role: Group Product Manager - BI and Data Science - Merchandising & Store Operations
Location: United States
City, State: Remote
Job Area: Full time
Job ID: 202632163
Job Category: Technology
At DICK'S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
If you are ready to make a difference as part of the world's greatest sports team, apply to join our team today!
OVERVIEW:
Overview
We are looking for a Group Product Manager to lead BI and Data Science product management for Merchandising, Planning, Inventory, and Store Operations. This leader will own the product strategy and roadmap for the intelligence products that help DSG teams plan assortments, manage inventory, optimize seasonal decisions, operate stores, and understand performance across the business.
The portfolio includes certified reporting, operational dashboards, planning and merchandising data products consumed through insights experiences, self-service analytics, and applied data science capabilities such as forecasting, optimization, recommendations, and decision support. This leader will partner across Merchandising, Planning, Stores, Supply Chain, Data Science, Analytics, Engineering, Architecture, and business leadership to translate complex operational needs into scalable products that improve speed, quality, and consistency of decision-making.
This is a hands-on product leadership role. The GPM will lead BI product managers, manage third-party PM capacity, establish portfolio discipline, and shape the future team structure as DSG expands its in-house product capability in this space.
Why this role matters
This role sits at the center of how DSG modernizes decision-making across merchandising and store operations. The leader will help move teams from fragmented reports, manual analysis, and reactive planning toward trusted reporting, governed metrics, self-service insights, and model-enabled recommendations that improve planning quality, inventory productivity, store execution, and athlete experience.
This is a rare opportunity to lead a high-impact intelligence portfolio at the intersection of retail operations, merchandising, planning, inventory, and applied AI. The role has direct visibility into how DSG makes some of its most important business decisions and will shape the products, operating model, and team structure that modernize decision-making across stores and merchandising.
Key Responsibilities
- Own the portfolio strategy and roadmap for BI, reporting, analytics, decision support, and applied data science across Merchandising, Planning, Inventory, and Store Operations.
- Lead store operations intelligence for senior store vice presidents, field leaders, store managers, store captains, and operational teams.
- Lead merchandising and planning intelligence for merchants, planners, buyers, inventory optimization teams, and seasonal or pre-season planning workflows.
- Partner with the Data Products GPM to connect trusted domain data foundations to BI, analytics, decision-support, and applied data science products.
- Define where data science should change decisions by identifying high-value opportunities for forecasting, optimization, recommendations, and model-enabled workflows.
- Translate ambiguous business asks into clear product problems, measurable outcomes, user workflows, prioritized roadmaps, and delivery plans.
- Modernize the reporting ecosystem by moving teams from fragmented dashboards, manual reconciliation, and one-off reporting toward certified metrics, semantic-layer alignment, self-service analytics, and scalable insights products.
- Establish clear portfolio governance including intake, prioritization, roadmap reviews, dependency management, release planning, adoption planning, and post-launch value tracking.
- Lead and develop product talent including in-house BI PMs and third-party PM resources, with consistent standards for product thinking, documentation, stakeholder alignment, and delivery accountability.
- Measure business impact through outcomes such as decision quality, planning cycle time, forecast accuracy, inventory productivity, store execution, adoption, reduced manual effort, and operational ROI.
QUALIFICATIONS:
- 10+ years of experience in product management, data product management, analytics product management, BI product leadership, data science product management, or related technical product roles.
- Experience leading product portfolios in merchandising, retail, stores, supply chain, inventory, planning, marketplace, finance, or operations-heavy environments.
- Strong product leadership across BI, analytics, metrics, reporting modernization, semantic layers, data products, and/or applied ML.
- Demonstrated ability to lead through ambiguity in highly matrixed organizations with senior business and technology stakeholders.
- Experience managing product managers, contractors, consultants, third-party PMs, or cross-functional delivery partners.
- Technical fluency in data platforms, BI tools, analytics engineering, data governance, experimentation, forecasting, optimization, and ML-enabled decision systems.
- Strong communication skills with the ability to translate between executive business needs, user workflows, data constraints, technical tradeoffs, and delivery roadmaps.
- Bachelor's degree in a relevant field required. Advanced degree in business, computer science, engineering, analytics, data science, or a related discipline preferred.
The range listed reflects the anticipated salary for this role, but we recognize that every candidate brings unique experience and skills. There may be flexibility based on individual qualifications, and we encourage open conversations about compensation throughout the hiring process.
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VIRTUAL REQUIREMENTS:
At DICK'S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments.
To ensure a smooth and secure experience, please note the following:
- Cameras must be on during all virtual interviews.
- AI tools are not permitted to be used by the candidateduring any part of the interview process.
- Offers are contingent upon a satisfactory background check which may include ID verification.
If you have any questions or need accommodations, we're here to help. Thanks for helping us keep the process fair and secure for everyone!
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At DICK'S Sporting Goods, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
DICK'S Sporting Goods AI Hiring
DICK'S Sporting Goods has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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