84.51° is looking for a Senior ML Engineer to develop technical solutions for assortment decision-making at Kroger, building packages and tools to enable reliable and efficient execution and delivery of assortment data and sciences to stakeholders.
Requirements
- 2+ years of experience using Python or Pyspark to develop analytical solutions.
- 2+ years of experience with big data tools, and architecture (e.g. Kubernetes, Spark).
- 2+ years of experience with data wrangling/cleaning/prep.
- Experience with cluster specification and query execution optimization in Databricks.
- Experience building or maintaining modular applications, pipelines, packages, or libraries.
- Passionate about technical solution development and code management best practices.
- Experience with neural networks a plus
Responsibilities
- Experience implementing and evolving MLOps best practices
- Data engineering, automating ML workflows, experiment tracking, model registration, accuracy monitoring, model serving.
- Evolve or develop methodologies and solutions for assortment and space optimization
- Special focus is on item performance prediction models and optimization algorithms.
- Support the technical development of foundational assortment sciences and data products end-to-end, including documentation, CI/CD deployments, and input/output QA checks
- Identifying opportunities for standardization & automation of existing solutions and processes.
- Working closely with key internal stakeholders to ensure SLA’s are met for model pipeline; output accuracy is consistent, reliable, and reproducible.
Other
- Effective alignment of stakeholders and deliverables using documentation to ensure on-time delivery and acceptable output.
- Partner with other technical experts (i.e. engineering, architecture) to identify and implement best practices in tech stack, tool usage, and code development.
- Demonstrates grit when solving complex and drawn-out methodological or scaling issues.
- Ability to work in a highly collaborative environment.
- Strong academic background in computer science, engineering, or similar discipline.
