About us:
As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers.
Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here, we believe your unique perspective is important, and you'll build relationships by being authentic and respectful.
Overview about TII
At Target, we have a timeless purpose and a proven strategy. And that hasn’t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target’s global team and has more than 4,000 team members supporting the company’s global strategy and operations.
Team Overview:
Our Supply Chain Data Science team oversees the development of state-of-the-art mathematical techniques to help solve important problems for Target’s Supply Chain e.g., identifying the optimal quantities and positioning of inventory across multiple channels and locations, planning for the right mix of inventory investments vs guest experience, digital order fulfillment planning, transportation resource planning, etc. As a Senior Data Scientist in Digital fulfilment space, you will have the opportunity to work with Product, Tech, and Business Partners to solve retail challenges at scale for our fulfillment network.
Position Overview:
As a Senior Data Scientist at Target, you will get an opportunity design, develop, deploy, and maintain data science models and tools. You’ll work closely with applied data scientists, data analysts and business partners to continuously learn and understand evolving business needs. You’ll also collaborate with engineers and data scientists on peer teams to build and productionize fulfillment solutions for our supply chain/logistics needs.
Develop a strong understanding of business/ operational processes within Target’s supply chain.
Develop an in-depth understanding of the various systems and processes that influence digital order fulfillment speed & costs.
Develop optimization-based solutions, approximate mathematical models (probabilistic/deterministic models) of real-world phenomena, predictive models and implement the same in real production systems with measurable impact.
Analyze large datasets for insights leading to business process improvements or solution development.
Develop and deploy modules for testing and validating multiple scenarios to evaluate the impact of various fulfillment strategies.
Adopt modular architecture and good solution development practices to enhance the overall product performance and guide other team members.
Produce clean, efficient code based on specifications.
Coordinate the analysis, troubleshooting and resolution of issues in the models and software.
About You:
Must Have:
We are looking for candidates who combine rigorous machine learning skills with a decent grasp of optimization and applied math. You should have:
Bachelor’s/Master’s/PhD in Computer Science, Mathematics, Statistics, Operations Research, or a related quantitative discipline.
4+ years of experience working across ML and optimization domains, ideally in dynamic, real-world problem spaces.
Strong coding skills in Python and experience working with Spark or distributed data frameworks.
Hands-on experience with ML modeling pipelines, from data ingestion and feature engineering to model development, evaluation, and deployment.
Knowledge of OR techniques (e.g., LP/MIP) and how to integrate them with ML systems.
Strong problem-solving skills with a track record of delivering solutions to complex, ambiguous business problems.
Ability to independently own and drive projects end-to-end in a collaborative environment.
Excellent communication skills—both technical and strategic—across engineering and business teams.
Experience with MLOps frameworks and practices (e.g., model versioning, CI/CD for ML, monitoring).
Exposure to supply chain, logistics, or fulfillment optimization problems.
Experience working in a production-scale environment with high data volume and business-critical pipelines.
Ability to build solution/ models that operate at low latency.
Know More About Us here:
- Life at Target- https://india.target.com/
- Benefits- https://india.target.com/life-at-target/workplace/benefits
- Culture- https://india.target.com/life-at-target/belonging
