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Sr Data Scientist- Supply Chain

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:

  • Bachelor’s/MS/PhD in Mathematics, Statistics, Operations Research, Industrial Engineering, Physics, Computer Science, or other related fields.

  • 3-4+ years of relevant experience in data sciences; experience with supply chain or e-commerce order fulfillment is preferred.

  • Excellent working knowledge of mathematical and statistical concepts, operations research, data structures and algorithms, data analysis, visualizations, and applications of the same in a variety of business problems.

  • Good working knowledge of machine learning, probability estimation methods, linear programming (integer, real and mixed integer), and their applications in industry settings

  • Strong analytical thinking and data visualization skills. Ability to creatively solve business problems, innovating new approaches where required.

  • Ability to clean, transform, manipulate, and analyze large datasets for insights leading to business process improvements or solution development. Background in handling supply chain data will be preferable but not mandatory.

  • Experience developing, testing, and maintaining large codebases in a collaborative environment.

  • while meeting industry best practices.

  • Highly proficient in programming skills in Python, SQL, Hadoop/Hive, Spark.

  • Experience deploying solutions with large-scale business impact in commercial setting is a plus.

  • Self-driven and results oriented. Willing to stretch to meet tight timelines.

  • Strong team player with ability to collaborate effectively across geographies/ time zones.

  • Excellent written and verbal communication skills

Know More About Us:

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

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Average salary estimate

$90000 / YEARLY (est.)
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$70000K
$110000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Sr Data Scientist- Supply Chain, Target

If you are an ambitious Sr Data Scientist looking for an exciting opportunity to make a big impact at Target, one of America's leading retailers, this could be the perfect role for you! Situated at Tower 02, Manyata Embassy Business Park in Bangalore, you’ll join our Supply Chain Data Science team, working with a bunch of talented individuals from diverse backgrounds who are redefining retail in a collaborative environment. Here at Target, we cherish every voice and value authentic relationships. In this position, you will design, develop, and maintain sophisticated data science models and tools that address real retail challenges, especially in our digital fulfillment space. Imagine diving into the intricacies of order fulfillment, inventory optimization, and transport resource planning - all vital aspects of our supply chain. Your analytical thinking will shine as you analyze large datasets to drive business process improvements and develop predictive models that have measurable impacts. Collaborating closely with product, tech, and business partners, you’ll navigate the complexities of inventory and contribute to building and deploying effective fulfillment solutions. If you have a strong foundation in mathematics, statistics, and programming languages like Python and SQL, along with experience in supply chain or e-commerce, we want to hear from you! Join Target, and let’s make a meaningful impact together while promoting a culture of care and respect.

Frequently Asked Questions (FAQs) for Sr Data Scientist- Supply Chain Role at Target
What are the key responsibilities of a Sr Data Scientist at Target?

As a Sr Data Scientist at Target, your key responsibilities will include designing and developing data science models tailored to optimize supply chain processes, analyzing large datasets for actionable insights, collaborating with multi-functional teams to understand evolving business needs, and maintaining data systems to ensure they remain robust and effective.

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What qualifications are necessary for the Sr Data Scientist role at Target?

A successful candidate for the Sr Data Scientist position at Target should ideally hold a Bachelor’s, Master’s, or PhD in Mathematics, Statistics, Operations Research, Computer Science, or related fields. With at least 3-4 years of experience in data science, familiarity with supply chain dynamics, and proficiency in programming languages like Python and SQL, you will be equipped to thrive in this role.

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What tools and technologies should a Sr Data Scientist at Target be proficient in?

A Sr Data Scientist at Target should have excellent working knowledge of Python, SQL, Hadoop/Hive, and Spark. Familiarity with machine learning techniques, data visualization skills, and the ability to manipulate and analyze large datasets are also crucial for successfully tackling challenges in our digital fulfillment and supply chain operations.

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How does teamwork factor into the role of a Sr Data Scientist at Target?

Teamwork is essential for a Sr Data Scientist at Target, as you'll frequently collaborate with applied data scientists, engineers, and business partners. Sharing insights, brainstorming solutions, and building fulfilling relationships across geographies ensures that you contribute effectively to the overarching goals of our supply chain strategy.

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What type of impact can a Sr Data Scientist at Target expect to have?

The impact a Sr Data Scientist can have at Target is significant. By developing optimization-based solutions and predictive models, you’ll directly influence key business processes, improve operational efficiency in supply chain logistics, and play a vital role in enhancing customer satisfaction through efficient order fulfillment.

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Common Interview Questions for Sr Data Scientist- Supply Chain
Can you explain your experience with supply chain data and its applications in your previous roles?

When answering this question, detail specific instances where your expertise in supply chain data drove meaningful outcomes. Highlight how you interpreted data to inform decisions, and describe any predictive models or optimization solutions you've developed to improve operations.

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How do you approach developing machine learning models for business problems?

Discuss your methodology for identifying the problem, gathering data, preprocessing it, and selecting appropriate algorithms. Illustrate how you've iterated on your models based on performance metrics to ensure their effectiveness in solving the business challenge.

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What tools have you used for data analysis and visualization, and how do you choose which one to use?

Be sure to mention specific tools like Python, SQL, and visualization software. Discuss how you assess the nature of the data and the specific insights required when selecting tools and frameworks for effective analysis and visualization.

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Describe a challenging data science project you worked on and what you learned from it.

Choose a project that posed significant challenges, describe your role, the hurdles you faced, and how you overcame them. Focus on your learning experience and how it informed your approach in future projects.

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How do you ensure the quality and reliability of your data models?

Discuss your commitment to validating and testing your models rigorously. Mention techniques such as cross-validation, backtesting, and the importance of maintaining clear documentation and code quality.

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How do you stay current with advancements in data science and machine learning?

Mention any communities you’re part of, conferences you attend, or journals you follow. Sharing specific examples of how you've applied new techniques or tools you've learned into your work will show your proactive approach to staying updated.

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How do you communicate complex data findings to non-technical stakeholders?

Highlight your communication strategy, which would include using clear visuals, simplifying technical jargon, and focusing on the business implications of data findings to ensure stakeholders understand the value.

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What role does experimentation play in your data science work?

Describe your philosophy around experimentation, including hypothesis testing, A/B testing, and the importance of learning from failure. Explain how experimentation helps in refining models and strategies.

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Can you walk us through a time you improved a business process with data analysis?

Provide a specific example where your analysis led to a tangible improvement in a process. Discuss how you identified the issue, the steps taken based on your analysis, and the resulting impact on the business.

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How familiar are you with programming languages relevant to this role, and can you share specific instances of their use?

Be prepared to discuss your programming skills, focusing on Python and SQL. Provide examples from your past projects where you applied these languages efficiently, perhaps in automating data processing or implementing machine learning models.

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DATE POSTED
April 22, 2025

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