Data Scientist - Retail Media

HEBSan Antonio, TX
7d

About The Position

H-E-B's Corporate Planning and Analysis Team develops and maintains budgets and financial systems while providing current, reliable financial data, analysis, and technical information. H-E-B Retail Media (HRM) is a new and exciting area of opportunity for H-E-B. We are a rapidly growing team of passionate and talented digital consultants working in a fun, fast paced and high growth environment. H-E-B Retail Media is quickly becoming a leader in the retail performance media industry. The vision is to become the advertising platform of choice for CPG brands by increasing the effectiveness of their marketing investments. We partner with some of the world's largest brands to drive their business with a variety of digital marketing and promotional strategies. This is a unique opportunity to join a small, high-visibility team within a very large organization. As a Data Scientist, your archetype is an ML / AI Expert + ML Engineer. Your passions include understanding best-in-class AI techniques, customizing algorithms to create HEB-unique differentiators, integrating AI techniques into H-E-B reusable end-to-end ML pipelines, refactoring DS algorithms and models based on the target platform to maximize platform efficiency, and optimizing the end-to-end learning pipeline (from exploration, development, building, and deploying to endpoint systems). Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and Customer service. 'Partner-owned' means our most important resources--People--drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company. Do you have a: HEART FOR PEOPLE... willingness to take a break from algorithmic thinking / detecting to translate and share your findings with a variety of business customers? HEAD FOR BUSINESS... understanding of best-in-class AI techniques? PASSION FOR RESULTS... ability to generate business-valued questions and data-driven solutions?

Requirements

  • A related degree or comparable formal training, certification, or work experience
  • 3+ years of experience in a retail or retail-related decision science role
  • Well-rounded experience integrating math / science and platform engineering
  • Proficiency in DS techniques (classification, regression, optimization)
  • Some familiarity with big data ecosystems (e.g., Spark, Hadoop) and UNIX commands / scripting
  • Understanding of best-in-class AI techniques to customize algorithms that created H-E-B-unique differentiators
  • Programming language skills (SQL, R, Python, Scala, Java, C/C++)
  • ML optimization skills (GPU code optimization, Horovod, SparkMLlib optimization, Cython, JNI, Numba
  • Mainstream ML / AI skills (deep learning, computer vision, NLP / NLU, reinforcement learning, meta-learning, federated learning)
  • Strong research and analytical skills
  • Critical and lateral thinking skills
  • Ability to grow expertise in constructing distributed machine learning pipeline from scratch
  • Work in a fast-paced retail environment with frequently shifting priorities
  • Work extended hours; sit for long periods

Responsibilities

  • Serves as an expert in selected mainstream ML / AI fields
  • Customizes open source ML / AI algorithms with solid proof from math reasonings
  • Extends best-in-class AI techniques from the latest ML / AI development, academic papers, industry / community; integrates those techniques to H-E-B reusable end-to-end ML pipelines; customizes algorithms to create H-E-B-unique differentiators
  • Optimizes end to end machine learning pipeline from exploration, development, building, deployment to endpoint systems)
  • Refactors DS algorithms and models based on the target platform to maximize platform efficiency
  • Applies an inquisitive nature about open source algorithms, their theories, and their implementation
  • Grows expertise in constructing distributed machine learning pipeline from scratch

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What This Job Offers

Job Type

Full-time

Career Level

Mid Level

Education Level

No Education Listed

Number of Employees

5,001-10,000 employees

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