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Sr Data Scientist - Demand Forecasting

Employer
Target
Location
Brooklyn Park, Minnesota
Start date
Apr 26, 2024
Closing date
May 12, 2024

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Category
Retail / Wholesale
Employment Status
Full Time
Description: The pay range is $93,800.00 - $168,800.00

Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.

JOIN TARGET AS A SENIOR DATA SCIENTIST - DEMAND FORECASTING

About us:

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

A role with Target Data Sciences means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Search, Recommendations, Demand Forecasting or Advanced Machine Learning/Gen AI teams, you'll be challenged to harness Target's impressive data breadth to build the algorithms that power solutions our partners in Marketing, Ad Tech, Personalization and Supply Chain Optimization rely on. Every Data Scientist on Target's AI team can expect modeling and data science, software development of highly performant code for model performance to elevate Target's culture and apply retail domain knowledge.

As a Senior Data Scientist on the Demand Forecasting team with Target, you'll develop models and algorithms for our retail business. Through your understanding of the foundations and working principles of machine learning algorithms, linear algebra, probability theory, statistics, and optimization theory - you will perform data exploration and analysis (look at distributions, find anomalies, create simple visualizations, etc.) and implement algorithmic solution given specifications, while adhering to best practices in model development. We will look to you to understand and actively follow foundational programming principles (best practices, know about unit tests, code organization, basics of CI/CD etc.) and create a well-maintainable and tested codebase with relevant documentation. You will get the opportunity to develop in one or more approved programming languages (Python, R, Java, Scala) and adhere to best practices in data analysis and data understanding.

Our team oversees the creation and development of the Demand Forecasting quantitative platform, building the key components which allow us to evaluate the demand and prices of our products and proprietary 'new ventures' products. We aim to develop these tools to be more efficient and precise - while also making them directly available to our business team users and Buyer, allowing us to quickly respond to our Guest needs in stores and online.

In this role at Target as a Senior Data Scientist, you'll:
  • Change the way demand forecasting and pricing is originated in retail in Target Data Sciences
  • Create our next generation demand and pricing platform at scale
  • Write code for math/stats models to evaluate demand for millions of items
  • Partner with business users and other scientists / engineers to build a framework allowing us to develop a new platform to more efficiently estimate demand and price on the fly
  • Impact our business by improving our ability to serve our Guests and by directly reducing compute cost through more efficient algorithms
  • Coordinate the analysis, troubleshooting, and resolution of issues in our software and the broader infrastructure


Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs.

About you:
  • 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent work experience
  • 3 plus years of experience building Time Series Forecasting models, Pricing models, ML models or optimization algorithms
  • Strong quantitative skills including specific experience with Statistical Inference, Generalized Linear Models, Markov Decision Processes
  • Proficient in one or more of Python, R, Java or equivalent programming language
  • Deep understanding of data structures and algorithm design
  • Ability to test and measure algorithms
  • Excellent written and verbal communication skills
  • Self-driven and results oriented with the ability to meet tight timelines
  • Collaborative team player with ability to partner effectively across time zones


This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target's needs. A Hybrid/Flex for Your Day work arrangement means the team member's core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.

Americans with Disabilities Act (ADA)

Target will provide reasonable accommodations with the application process upon your request as required to comply with applicable laws. If you have a disability and require assistance in this application process, please visit your nearest Target store or Supply Chain Facility or reach out to Guest Services at 1-800-440-0680 for additional information.

Application deadline is : 05/09/2024
Qualifications:

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