Data Scientist II
Listed on 2026-10-26
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IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist, AI Business & Operations
H-E-B, LP
Job Location:2416 E 6th St., Austin, TX 78702. Employer will allow hybrid work schedule.
Job Title:Data Scientist II
Minimum Salary:Education:
Bachelor's degree in Economics or similar quantitative field.
SOC Code:15-2051
SOC OccupationTitle:
Data Scientists
Duration:Regular Hire
Work week:Full-time
Supervise employees:No
Travel Required:Yes - travels to San Antonio, TX once per quarter
Experience:3 years of experience in retail data science. Requires skills and experience to involve:
- Expertise in advanced machine learning and artificial intelligence applied in the retail domain.
- Demonstrated experience in designing novel machine learning solutions, including multi-modal and distributed systems.
- Demonstrated experience with large-scale data engineering and end-to-end production machine learning pipelines.
- Knowledge of financial planning and omnichannel retail applications of machine learning and artificial intelligence.
- Demonstrated experience in developing and advancing enterprise machine learning roadmaps and scalable artificial intelligence capabilities.
Design, develop, and implement machine learning and artificial intelligence solutions to support business objectives in the retail domain. Build end-to-end data and model pipelines integrating multiple data sources and systems. Develop, test, and optimize machine learning algorithms and distributed processing workflows using sound mathematical and statistical methods. Evaluate and adapt open-source and proprietary algorithms to meet business and technical requirements. Analyze and communicate model results and analytical findings to business and cross-functional stakeholders.
Collaborate with technical and business teams to align AI/ML solutions with operational and financial goals. Improve model and pipeline performance using scalable computing tools and frameworks. Apply knowledge across AI domains, including deep learning, computer vision, natural language processing, generative AI, reinforcement learning, and federated learning. Support the ongoing enhancement of enterprise machine learning and artificial intelligence capabilities.
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