Data Science Intern at SRS Distribution in McKinney, TX

Position Purpose 

The Summer Data Science Intern will support the Data, AI & Innovation team in analyzing business data across sales, pricing, supply chain, customer, and branch operations domains. This internship provides hands-on experience applying data science, analytics, and machine learning techniques to real-world business problems. 

The intern will work closely with data scientists, data engineers, and business stakeholders to assist in exploratory analysis, model development, dashboard creation, and experimentation related to AI and advanced analytics initiatives. 

This role is designed to provide meaningful project ownership, mentorship, and exposure to production-grade analytics environments. 

Key Responsibilities 

• Assist in analyzing structured and unstructured datasets from supply chain, sales, pricing, and customer domains. 

• Perform data cleaning, transformation, and exploratory data analysis (EDA). 

• Support development of machine learning based predictive models, analytical solutions, and optimization under guidance from senior team members. 

• Contribute to building dashboards or reports to support business decision-making. 

• Participate in ongoing Generative AI (GenAI) initiatives, including experimentation with LLM-based tools. 

• Document methodologies, findings, and recommendations. 

• Present project outcomes and insights to stakeholders at the end of the internship. 

Potential Internship Projects 

The intern may contribute to one or more of the following high-impact initiatives: 

1. Category Optimization 

• Analyze product category performance across branches and customer segments. 

• Identify opportunities for assortment rationalization or expansion. 

• Build optimization models to balance supplier selection and minimize Cost of Goods Sold (COGS). 

• Build dashboards highlighting category KPIs (margin, turnover, attachment rate). 

2. Product Recommendation Engine 

• Assist in building or enhancing product recommendation models using historical order data and product attributes. 

• Perform customer-product affinity analysis. 

• Explore collaborative filtering or rule-based recommendation techniques. 

• Evaluate model performance and business impact. 

3. Segmentation Workbench 

• Develop customer or branch segmentation models using clustering techniques. 

• Create a reusable segmentation framework for business teams. 

• Analyze behavioral, geographic, and purchasing patterns. 

• Build visualization tools to enable stakeholder exploration of segments. 

4. Sales Team Support Analytics 

• Develop insights to help sales representatives identify cross-sell and upsell opportunities. 

• Analyze win/loss data and customer purchasing trends. 

• Create opportunity-prioritization models. 

• Build performance dashboards to support sales planning. 

5. Branch Operations Analytics 

• Analyze branch-level performance metrics. 

• Identify operational inefficiencies or demand trends. 

• Support forecasting efforts for inventory and staffing. 

• Provide data-driven insights to improve branch productivity. 

6. Customer Churn Prediction 

• Analyze historical customer behavior to identify churn patterns and risk indicators. 

• Develop and evaluate predictive models to estimate churn probability. 

• Engineer features from transactional, engagement, and service data. 

• Identify high-risk customer segments and recommend targeted retention strategies. 

• Present actionable insights to sales and customer success teams to reduce churn and improve lifetime value. 

Learning Objectives 

By the end of the internship, the intern will: 

• Gain practical experience in applying data science to real business problems. 

• Understand the end-to-end lifecycle of analytics projects. 

• Learn how to translate business requirements into technical solutions. 

• Improve proficiency in Python, SQL, and data visualization tools. 

• Gain exposure to machine learning workflows and potentially Generative AI applications. 

• Develop professional communication and presentation skills. 

Measurable Deliverables 

By the end of the internship, the intern will be expected to deliver: 

• Defined Business Problem Statement with measurable success criteria 

• Cleaned & Documented Dataset with data dictionary 

• Exploratory Data Analysis (EDA) Summary Report 

• Production-Ready Jupyter Notebook or Python Script with: 

• Modular, well-commented code 

• Reproducible workflow 

• Model evaluation metrics 

• Model Performance Report (if applicable), including: 

• Baseline comparison 

• Evaluation metrics (AUC, RMSE, precision/recall, etc.) 

• Business interpretation of results 

• Executive-Level Presentation (15–20 minutes) summarizing: 

• Business context 

• Approach & methodology 

• Key findings 

• Business impact 

• Recommended next steps 

• Documentation Package suitable for handoff to full-time team members 

Qualifications 

Minimum Qualifications 

• Currently pursuing a Master’s or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. 

• Advanced proficiency in Python and/or SQL. 

• Foundational understanding of statistics and data analysis. 

• Strong analytical and problem-solving skills. 

• Ability to work in a collaborative, team-oriented environment. 

• Experience with machine learning coursework or academic projects. 

• Familiarity with libraries such as pandas, scikit-learn, TensorFlow, or PyTorch. 

• Prior internship, research, or project experience in analytics.