84.51° is looking to apply machine learning, natural language processing, and modern AI frameworks to create scalable, intelligent customer solutions by analyzing first-party retail data.
Requirements
- Experience querying data from relational databases using SQL.
- Experience (academic projects, internships, or research) using R, Python, or other similar statistical software to develop analytical solutions.
- Exposure to data wrangling, cleaning, and dimensionality reduction techniques.
- Foundational understanding of machine learning concepts (classification, regression, clustering).
- Experience with (academic projects, internships, or research) Big Data concepts, tools, and architecture (e.g. Spark, Databricks, Pytorch).
- Natural Language Processing (NLP) and Large Language Models (LLMs): Exposure to prompt engineering, intent extraction, or modern LLM APIs (OpenAI, Hugging Face).
- Semantic Search & Embeddings: Familiarity with vector databases and embedding models for product/theme matching and retrieval.
Responsibilities
- Partner with senior data scientists and engineers to develop and test audience creation and recommendation solutions, including natural language–driven workflows.
- Query, clean, and transform large-scale customer datasets (loyalty, clickstream, digital interaction data) to support audience modeling and campaign targeting.
- Apply foundational statistics and machine learning techniques to measure customer behavior and campaign performance.
- Build and share insights and visualizations that translate technical findings into clear customer and business stories.
- Follow best practices for coding, quality assurance, version control, and documentation to ensure work can be scaled and reused.
- Actively participate in team discussions, retrospectives, and knowledge-sharing sessions to accelerate your learning and contribute to team success.
- Package building and code optimization experience or a strong desire to learn.
Other
- Bachelor's degree in a quantitative field (Statistics, Data Science, Computer Science or related discipline).
- Strong communication skills, with the ability to explain technical ideas to non-technical audiences.
- Curiosity, adaptability, and a strong desire to learn from senior data scientists and cross-functional partners.
- Ability to work in a highly collaborative environment.
- Grocery and/or retail experience is a plus.
