What is a Data Scientist at DICK'S Sporting Goods?

As a Data Scientist at DICK'S Sporting Goods, you are at the forefront of transforming how the nation's leading sports retailer interacts with millions of athletes. Your work directly influences everything from personalized customer experiences and dynamic pricing to complex supply chain logistics and inventory optimization. By leveraging vast amounts of retail and customer data, you help ensure the right products are in the right stores at the right time.

This role is critical to the continued digital and omnichannel evolution of DICK'S Sporting Goods. You will not just be building models in a vacuum; you will be solving high-impact, real-world retail challenges. Whether you are optimizing predictive models for e-commerce or designing algorithms to streamline brick-and-mortar operations, your insights will drive strategic business decisions across the enterprise.

Expect a fast-paced environment where scale and complexity meet. You will collaborate closely with cross-functional teams within the data analytics department, engineering, and product management. To succeed here, you must be as passionate about understanding the retail landscape as you are about writing clean, efficient code and deploying robust machine learning models.

Common Interview Questions

The following questions are representative of what candidates face during the DICK'S Sporting Goods interview process. While you should not memorize answers, use these to understand the patterns and expectations of our technical and behavioral rounds.

SQL and Python Coding

These questions test your raw ability to manipulate data. Remember that you may not have access to advanced functions, so practice writing fundamental, syntax-perfect code.

• Write a SQL query to find the second highest purchasing customer in each region without using the WITH clause or window functions.
• Given a list of dictionaries representing store inventory, write a Python function to aggregate the total count of each item category.
• How do you optimize a slow-running SQL query if you cannot create new indexes or use temporary tables?
• Write a Python script to parse a messy log file and extract specific error codes using basic string manipulation.
• How would you handle a situation where your Python code is throwing a memory error while processing a large dataset?

Machine Learning and Modeling

These questions evaluate your theoretical knowledge and practical application of machine learning algorithms to retail scenarios.

• Explain the mathematical difference between Ridge and Lasso regression. When would you use each?
• Walk me through a time you had to choose between a simpler, interpretable model and a complex, black-box model.
• How would you approach building a predictive model for seasonal inventory demand?
• Describe your process for handling missing data in a dataset with millions of rows.
• What metrics would you use to evaluate a product recommendation engine on our website?

Real-Life Problem Solving & Resume Deep Dive

These questions assess your ability to navigate ambiguity, structure business problems, and communicate your past experiences effectively.

• Tell me about a project on your resume that failed or did not meet expectations. What did you learn?
• Here is a pen and paper: sketch out the logic for an algorithm that identifies fraudulent online returns.
• We want to increase foot traffic in our brick-and-mortar stores. How would you use data to tackle this problem?
• Describe a time you had to explain a complex statistical concept to a business stakeholder who had no technical background.
• If you were given a dataset with no documentation, how would you begin your exploratory data analysis?

Getting Ready for Your Interviews

Thorough preparation is your best strategy for navigating our interview process. We evaluate candidates across several core dimensions to ensure they can thrive in our data-driven environment.

• Technical Proficiency – We assess your fundamental coding skills in Python and SQL. Interviewers look for your ability to write clean, logical code without relying heavily on advanced built-in functions or complex syntactic shortcuts.
• Machine Learning & Applied Modeling – You need a solid grasp of machine learning concepts and the ability to apply them to real-world retail problems. We evaluate your past projects, your understanding of model trade-offs, and your ability to deploy scalable solutions.
• Problem-Solving & Ambiguity – Retail data is inherently messy and business requirements can be vague. We test your ability to take an ambiguous, real-life problem, structure it logically, and design a data-driven solution, sometimes using nothing but a pen and paper.
• Communication & Business Acumen – A great model is useless if it cannot be explained to stakeholders. We look for candidates who can articulate complex technical concepts to non-technical audiences and align their data strategies with overall business objectives.

Interview Process Overview

The interview process for a Data Scientist at DICK'S Sporting Goods is designed to test both your fundamental technical skills and your ability to apply them to retail scenarios. Candidates typically begin with an automated online coding assessment, which focuses heavily on SQL and Python. This technical screen is strictly timed, usually lasting between one to two hours, and requires you to solve problems independently.

If you pass the initial screen, you will move on to the core interview stages. This often takes the form of a "super interview"—a condensed, high-intensity block of time consisting of multiple back-to-back sessions. You will meet with various members of the data analytics department, answering a mix of technical, behavioral, and resume deep-dive questions.

For the final stages, expect a semi-technical or behavioral round that may involve solving vague, real-world problems. You might be asked to step away from the keyboard and use a pen and paper to walk through your logic. We value candidates who remain composed, ask clarifying questions, and drive the conversation forward even when the prompt is open-ended.

This visual timeline outlines the typical progression from the initial automated coding screen through the final panel interviews. Use this to pace your preparation, focusing heavily on raw coding fundamentals early on, and shifting toward business case structuring and communication skills for the later rounds. Note that specific stages may vary slightly depending on the exact team or location, such as our Coraopolis, PA headquarters.

Deep Dive into Evaluation Areas

To succeed, you need to understand exactly what our interviewers are looking for in each phase of the evaluation. Below are the core areas you must master.

Coding and Data Manipulation (Python & SQL)

Your ability to extract, manipulate, and analyze data is the foundation of this role. We test your SQL and Python skills rigorously, often in a timed, automated environment like Codility. Strong performance here means writing accurate, efficient code under pressure.

Be ready to go over:

• Complex Joins and Aggregations – Writing SQL queries to merge multiple large datasets and extract meaningful metrics without using Common Table Expressions.
• Data Cleaning in Python – Handling missing values, transforming data types, and filtering datasets using core Python logic.
• Algorithm Optimization – Writing loops and conditional logic efficiently, as brute-force methods may time out.
• Debugging under constraints – Troubleshooting your own code when console output is limited or unhelpful.

Example questions or scenarios:

• "Write a SQL query to find the top three selling products per region, without using temporary tables or CTEs."
• "Given a raw dataset of customer transactions, write a Python script using only basic libraries to calculate the rolling average of purchase values."

Machine Learning and Project Deep Dives

We want to know that you understand the "why" behind the models you build, not just the "how." Interviewers will probe deeply into the projects listed on your resume to evaluate your end-to-end understanding of the machine learning lifecycle.

Be ready to go over:

• Model Selection and Trade-offs – Explaining why you chose a specific algorithm (e.g., Random Forest vs. XGBoost) for a past project and how you tuned it.
• Feature Engineering – Discussing how you transformed raw data into predictive features, particularly in a business context.
• Evaluation Metrics – Justifying your choice of metrics (e.g., Precision, Recall, RMSE) based on the specific business problem.
• Productionization – Explaining how your models were deployed, monitored, and maintained in a live environment.

Example questions or scenarios:

• "Walk me through the most complex machine learning project on your resume. What was the baseline, and how did you improve upon it?"
• "If we want to predict customer churn, what features would you engineer from our transaction logs, and how would you handle class imbalance?"

Real-World Problem Solving and Ambiguity

In the retail industry, data problems are rarely packaged neatly. We evaluate your ability to handle vague prompts, structure your thinking, and design solutions without the aid of a computer.

Be ready to go over:

• Business Case Structuring – Breaking down a high-level business goal (e.g., "improve inventory turnover") into a concrete data science problem.
• Logical Frameworks – Designing an analytical approach step-by-step, explaining your assumptions along the way.
• Handling Incomplete Information – Asking the right clarifying questions when presented with a scenario that lacks specific details.

Example questions or scenarios:

• "How would you design a recommendation engine for a new line of athletic footwear with no historical sales data?"
• "Map out the logic for a dynamic pricing model that adjusts based on real-time inventory levels and competitor pricing."

Key Responsibilities

As a Data Scientist at DICK'S Sporting Goods, your day-to-day work is a blend of deep technical execution and strategic business alignment. You are primarily responsible for designing, building, and deploying machine learning models that solve core retail challenges. This includes developing predictive algorithms for demand forecasting, creating personalized product recommendations for our e-commerce platform, and optimizing pricing strategies across our retail network.

Collaboration is a massive part of your role. You will work within a specialized data analytics department, but you will frequently partner with software engineers to productionize your models and with business stakeholders to define project requirements. You must be able to translate complex data findings into actionable insights that store operations, marketing, and supply chain teams can use immediately.

You will also spend a significant amount of time wrangling messy, disparate data sources. From point-of-sale transaction logs to online clickstream data, you are expected to clean, transform, and analyze massive datasets to uncover hidden trends that drive revenue and improve the athlete experience.

Role Requirements & Qualifications

To be a competitive candidate for the Data Scientist position at DICK'S Sporting Goods, you must demonstrate a strong mix of technical rigor and retail intuition.

• Must-have skills – Expert-level proficiency in SQL and Python. You must be comfortable writing complex queries and scripts from scratch. A deep understanding of core machine learning algorithms (regression, classification, clustering) and statistical analysis is non-negotiable.
• Experience level – We typically look for candidates with a proven track record of deploying models into production. Experience presenting technical findings to non-technical stakeholders is essential.
• Soft skills – Exceptional communication skills, a high tolerance for ambiguity, and the ability to self-start. You must be resilient and adaptable, especially when dealing with legacy systems or vague business requirements.
• Nice-to-have skills – Prior experience in retail, e-commerce, or supply chain analytics. Familiarity with cloud platforms (like GCP or AWS) and experience working in restrictive or heavily governed coding environments will give you a distinct advantage.

Frequently Asked Questions

Q: How long does the initial coding assessment take, and what is the format? The automated coding screen typically lasts between one and two hours. It is an independent, recorded session focusing heavily on SQL and Python. Be prepared to complete it within a few days of receiving the automated email.

Q: Why do some built-in functions or SQL features not work during the technical exam? Our assessment environment is sometimes configured to test your fundamental logic and problem-solving skills without the crutch of advanced shortcuts. Practice writing "vanilla" SQL (no CTEs or temp tables) and base Python to ensure you are not caught off guard.

Q: What is the "super interview" format? The super interview is a condensed panel format, usually consisting of three back-to-back 30-minute sessions with different members of the data analytics department. It is designed to evaluate your technical skills, cultural fit, and business acumen efficiently.

Q: What should I do if an interview question feels vague or unrelated to the role? Ambiguity is a common theme in our interviews, reflecting the real-world nature of retail data. If a question feels unclear, take the lead. Ask clarifying questions, state your assumptions, and structure a logical approach using a pen and paper if necessary.

Q: How long does the overall interview process take? The timeline can vary, but generally, it takes a few weeks from the initial coding screen to the final round. Because communication can sometimes be delayed, we recommend staying proactive and following up with your recruiter if you haven't heard back after a week.

Other General Tips

• Master the Fundamentals: Do not rely solely on Pandas or complex SQL wrappers. Ensure you can write basic loops, dictionaries, and standard JOIN and GROUP BY statements flawlessly from memory.
• Drive the Conversation: If an interviewer presents a vague scenario, do not wait for them to spoon-feed you details. Take charge, outline your framework, and show them how you tackle ambiguous problems head-on.
• Connect Data to Retail: Always tie your technical answers back to the business impact. Whether you are optimizing a query or tuning a model, explain how it ultimately benefits DICK'S Sporting Goods and our athletes.
• Prepare for Technical Glitches: Automated platforms can sometimes be buggy. If your console errors out and you cannot debug, stay calm, comment your code thoroughly to explain your logic, and submit your best effort.
• Know Your Resume Inside and Out: Expect deep, probing questions about every project you list. Be prepared to defend your technical choices, explain your methodology, and discuss the final business outcomes.

Summary & Next Steps

Joining DICK'S Sporting Goods as a Data Scientist is an opportunity to drive massive impact at the intersection of retail and technology. Your work will directly shape how millions of athletes discover and purchase the gear they love. While the interview process is rigorous and sometimes unpredictable, it is designed to identify resilient problem-solvers who possess both deep technical expertise and strong business intuition.

Focus your preparation on mastering fundamental SQL and Python syntax, deeply understanding your past machine learning projects, and practicing how to structure ambiguous business cases. Embrace the challenges of the restrictive coding environments and the open-ended onsite questions—they are your chance to showcase your adaptability and logical thinking.

This compensation data provides a baseline for what you can expect as a Data Scientist at DICK'S Sporting Goods. Use these insights to understand the total rewards package, including base salary and potential bonuses, so you can navigate the offer stage with confidence.

We believe in your potential to succeed. Keep refining your skills, leverage the insights and practice resources available on Dataford, and approach each interview stage with confidence. Good luck!