Data Sciences

Our Data Sciences team is responsible for building, governing, and maximizing the use of Target’s data assets for elevated decision-making across our retail operations. We build certified data sets for analytics and decision modeling and develop data platforms for exploration, visualization and measurement. We create statistical forecasting models, optimize algorithms with machine learning techniques, and validate model performance – with both analytical AI and generative AI – all while delivering algorithmic decisioning at massive scale. We empower our Target team with the insights they need to improve product and system performance and further innovate to improve guest experience.

Target Data Sciences logo with an atom icon on the left and the text "Data Sciences" on the right of the icon

Recent blogs

  • A photo of the Target sign on the exterior of a storefront.

    Scaling Marketing Campaign Forecasting with Generative AI

    April 9, 2026
    By Nikhil Rasineni and Utsav Mishra
    A retrieval-augmented approach allows us to reduce manual intervention and improve offer forecasting.
  • An illustration showing a computer screen, boxes and Target truck outside a warehouse.

    The Math Behind Target’s Inventory and Delivery Optimization

    November 18, 2025
    By Rajshekhar Singhania, Vinayaka Yaji, and Aashimi Bhatia
    Learn how Target leverages advanced mathematical models to optimize inventory placement and speed up delivery.

  • Unlocking the Power of Synthetic Digital Orders in Retail

    July 22, 2025
    By Aviral Maheshwari
    Learn how synthetic digital orders powered by AI are transforming forecasting and fulfillment efficiency at Target.
  • An image showing recommendations for cereal bowl accessories returning similar items using the earlier model vs. a wider array of products using the new model.

    Improving Accessory Recommendations with LLMs at Target

    July 1, 2025
    By David Relyea, Rankyung Park, Alina Nesen, Amit Pande, AJ Dabruzzi, and Longjiang Yang
    This GenAI-based algorithm makes it easier than ever to pair the right accessory with the right product.
  • A woman sitting with a laptop on her lap and looking at her Target shopping cart with recommended products to buy again.

    Elevating Guest Repurchasing Behavior Using Buy It Again Recommendations – Part 2

    March 4, 2025
    By Pushkar Chennu, Prathyusha Kanmanth Reddy, Rankyung Park, David Relyea, and Amit Pande
    This article introduces a new model for BIA recommendations using item- and category-level Hawkes processes.

  • The importance of data quality in decision-making

    January 31, 2025
    By Sweta Suman
    Maintaining high-quality data for accurate analytics, informed decision-making, and cost-effectiveness is key.
  • Example of Inventory Not Found showing a check out screen on a phone with laundry detergent, a cartoon style image of a Target store, and an image of shelving with a question mark where the product should be

    Predictive Modeling for Availability of Inventory

    July 24, 2024
    By Atul Goel and Sohini Mitra
    Read more on how Target's data sciences team uses data segmentation to improve product availability
  • Graph showing the probability of "Action A" with branches pointing to two potential actions labeled "Random Action" and "Greedy Action." To the right of this section, there's a large red box labeled "AGENT" with three possible paths labeled "action-chosen offer," "Reward-observed reward," and "observation-state, guest and offer context"

    Contextual Offer Recommendations Engine at Target

    July 11, 2024
    By Utsav Mishra
    Learn more about how Target's CORE recommends personalized offers to guests
  • Target data pipeline framework architecture diagram, showing ingestion, persistence, monitoring and alerts on the top of the image, the framework and associated metrics and metadata services in the middle of the diagram, and the pipeline process on the bottom showing data prep, validation, transformation, decoration, enrichment, and persistence flowing up into the framework

    Creating Certified Datasets

    May 16, 2024
    By Brad Thompson, Natarajan Ramamurthy, and Vinay Joshi
    How Target data scientists built a new data pipeline framework
  • Image titled "Compatible Items" with four products pictured side-by-side, (from left to right) child car seat, car mirror, seat protector, and seat organizer, with a "add all 4 to cart" call to action in a red button at the bottom of the image

    Bundled Product Recommendations

    April 23, 2024
    By David Relyea, Samaneh Karimi, and Amit Pande
    Learn more about how Target uses bundled product recommendations to enhance shopping experiences for our guests
  • Diagram showing the installed Poetry instance, with sections for "other version managers," "system," and "pyenv" with Python versions shown flowing to apps, pipx, and poetry

    Make Python DevEx

    March 29, 2024
    By Colin Dean
    How a 47+ year old tool can MAKE Python developer experience easier.
  • a person's hand holding a smart phone with a grocery list on the screen, including items like apples, orange juice, granola, and eggs

    Elevating Guest Repurchasing Behavior Using Buy It Again Recommendations

    November 2, 2023
    By Amit Pande and Rankyung Park
    Target’s Data Science team shares an inside look at our Buy It Again model.
  • Team member scans inventory in Target electronics aisle

    Solving for Product Availability with AI

    October 24, 2023
    By Brad Thompson and Meredith Jordan
    Read about how Target uses AI to improve product availability in stores.
  • TAC Architecture model showing a sequence dataset on the left with an arrow flowing right to a multi-layered model training, with an arrow to the right pointing to a representation of real time recommendations with model files and recommended items

    Target AutoComplete: Real Time Item Recommendations at Target

    July 25, 2023
    By Bhavtosh Rath
    A look at our Data Science team's patent-pending AI recommendation model