Learn about our business by attending meetings, huddles, and training.
Share creative ideas that will help improve our business.
Deliver reports, analyze metrics, and summarize information to help drive our team forward.
Assist in creating materials and/or presentations for meetings.
Take notes during meetings and provide recaps.
Collaborate with data scientists and data engineers to build, evaluate, and refine AI and machine learning models, with a focus on large language model (LLM) applications and retrieval-augmented generation (RAG) pipelines.
Assist in designing and implementing data preprocessing, feature engineering, and model evaluation workflows.
Write clean, well-documented code in Python (or other relevant programming languages) to support data analysis, model prototyping, and automation tasks.
Leverage AI-powered coding and productivity tools (e.g., Claude Code, GitHub Copilot) to accelerate development and maintain code quality.
Partner with internal clients and business stakeholders to understand requirements, translate business questions into analytical frameworks, and communicate findings clearly.
Perform exploratory data analysis and apply statistical methods to extract insights from structured and unstructured data sources.
Query and manipulate data from relational databases and other data stores to support analysis and model development.

Requirements

Currently pursuing a Bachelor's (Senior Level) or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
Foundational understanding of large language models (LLMs), including concepts such as prompt engineering, fine-tuning, embeddings, and token-based architectures.
Familiarity with retrieval-augmented generation (RAG) patterns and their application in building context-aware AI systems.
Solid grounding in basic statistics and probability, including hypothesis testing, regression analysis, distributions, and exploratory data analysis.
Hands-on programming experience in Python (preferred) or another language commonly used in data science (e.g., R, SQL, Julia).
Working knowledge of relational databases and SQL for data querying and manipulation.
Experience with or willingness to adopt AI-assisted coding tools such as Claude Code, GitHub Copilot, Cursor, or similar.
Experience with machine learning libraries and frameworks such as scikit-learn, PyTorch, TensorFlow, or Hugging Face Transformers.
Exposure to cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker).
Experience with version control systems (Git) and collaborative development workflows.
A portfolio of personal projects, coursework, or open-source contributions demonstrating applied data science or AI work.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonRSQLJuliascikit-learnPyTorchTensorFlowHugging Face Transformersdata preprocessingfeature engineering

Soft Skills

collaborationcommunicationcreativityanalytical thinkingnote-takingreportingpresentation skillsproblem-solvingadaptabilityteamwork