Interested in this Data Scientist role at DICK'S Sporting Goods?
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About This Role
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:
Are you a passionate technologist with experience in AI, Machine Learning, Data Science and Analysis? Are you looking for an opportunity to drive enterprise impact and shape the future of a leading sports retailer with $12B\+ in revenue and 800\+ physical stores? Do you enjoy working with a highly skilled team of Machine Learning engineers \& Scientists, co\-creating enterprise grade AI capabilities?
### JOB PURPOSE:
As the Lead Data Scientist \- Merchandising \& Pricing, you will be a key technical leader in our teammate transformation that aims to deliver a best\-in\-class teammate experience by providing them advanced intelligent decisioning tools using AI/GenAI and Machine Learning at its core. This is an exceptional opportunity not only to transform the way we deliver omnichannel Merchandising and Pricing by building foundational AI/GenAI capabilities, but also to do career defining work in the space.
This role will require an emerging technical leader \& SME with strong experience in traditional Machine Learning algorithms along with deep understanding of the cutting edge SOTA AI/GenAI methods used in Retail merchandising and Pricing data science initiatives. As a technical leader you will be influencing critical enterprise technical strategies both in the Machine Learning/AI space and neighboring spaces like forecasting, optimization, NLP, webservices, integrations with applications and data systems etc. You will partner with product, business, and engineering leads to design and implement data science powered intelligent tools for merchandising and pricing business partners and scale and help them understand the art of the possible with AI technology through deep technical design.
### RESPONSIBILITIES:
- Advanced Data Science Leadership: Lead design and implementation of advanced data science algorithms that improve merchandising and pricing business decisions, including building models for Demand forecasting, Assortment optimization, Price elasticity, and Inventory allocation and replenishment.
- Developing \& Optimizing Demand Forecasting models: Designing and deploying demand forecasting algorithms that go beyond univariate time series to multivariate and hierarchical forecasts for predicting long range, multi\-echelon sales forecasting, and that can handle cold start problems, reconciliation at all levels and works at scale.
- Assortment Planning \& Optimization: Develop \& Implement AI/ML driven assortment selection algorithms that learn from user behavior \& preferences to deliver tailored assortment choices based on user metadata like location, past site behavior etc. and that are optimized for the capacity, variety, sales targets and other business constraints.
- Natural Language Process (NLP) \& GenAI: Collaborate with product \& data engineers to identify data for modeling, and transform datasets as required for effective modeling, like creating identifying and enriching product attributes using NLP and LLMs. Creating feature stores and vector embedding used for Product associations and segmentation, and other modeling needs.
- Machine Learning \& Deep Learning: Build, scale and deploy robust Machine Learning models leveraging Classification, Regression, and Clustering, Context understanding, techniques to drive data\-driven decision\-making across diverse retail business functions. Leverage deep learning models for building complex forecasting and other predictive use cases.
- Price Elasticity \& Casual Inference: Develop models to process historical and large datasets to understand model Price elastic demand for products, categories, channels and customer segments using predictive and causal modeling techniques. Deliver actionable elasticity estimates and counterfactual analyses to inform pricing optimization, promotional strategies, and markdown decisions to monitor the performance of forecasting and other predictive models in real time, detect anomalies, ensuring data drift, concept drift, and addressing technical issues to maintain the efficiency \& effectiveness of model predictions.
- Experimentation \& A/B Testing: Collaborate with analytics, product and business teams to champion a test\-and\-learn approach by designing and executing structured experiments to validate model hypotheses, measure business impact, and drive continuous improvement.
- Research \& Development of Emerging Technologies: Staying updated with the latest advancements in AI, ML technologies and exploring opportunities to incorporate these innovations into Merchandising and Pricing transformation initiatives.
### PREFERRED QUALIFICATIONS:
- Master's Degree or Equivalent Level in quantitative fields like computer science, engineering, physics, mathematics, etc.
- 6\+ years of experience in the field with at least 2\-3 years of being the main technical lead in related projects
- Experience working with SOTA machine learning, deep learning (LSTM, Transformers), Optimization models for retail and ecommerce use cases driving efficiency in operations and customer value.
- Experience with Large Language models and Generative AI and Agents. Bonus if specific experience in operations research.
- Experience in ML Ops model monitoring, retraining, CI/CD, and experiment tracking
- Extensive experience using common machine learning and deep learning frameworks such as TensorFlow, PyTorch, OpenAI, and LangChain
- Expert understanding of Python and other common languages.
- Expert level experience in cloud platforms like Databricks, GCP, and offers like Azure ML, Vertex AI.
- Experience being the technical lead of multiple projects at the same time, responsible for delivery and business metrics
- Experience in an Agile working environment and at least one related project management tool (Azure, DevOps, Jira, etc.)
- Previous experience mentoring, training, and developing junior members of the team through technical influence.
- Experience with software engineering principles as it relates to Machine Learning systems.
- Comfortable presenting results to and influencing senior and executive leadership on strategic technical decisions, from the lens of science.
- Brings a collaborative, problem solving and growth mindset to all interactions with a strong focus on delivery.
QUALIFICATIONS:
- Education: Master's Degree or equivalent level preferred
- General Experience: Substantial general work experience together with comprehensive job related experience in own area of expertise to fully competent level. (Over 6 years to 10 years)
\#LI\-FD1
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!
Targeted Pay Range: $95,200\.00 \- $158,800\.00\. This is part of a competitive total rewards package that could include other components such as: incentive, equity and benefits. Individual pay is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all teammate pay regularly to ensure competitive and equitable pay.DICK'S Sporting Goods complies with all state paid leave requirements. We also offer a generous suite of benefits. To learn more, visit www.benefityourliferesources.com.
Salary Context
This $95K-$158K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At DICK'S Sporting Goods, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 38% below the category median. Disclosed range: $95K to $158K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
DICK'S Sporting Goods AI Hiring
DICK'S Sporting Goods has 81 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Latham, NY, US, Tulsa, OK, US, Burnsville, MN, US. Compensation range: $39K - $158K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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