Associate Data Scientist – Modeling, Analytics & Pipelines

Partner with product and domain experts to translate ambiguous business questions into clear analytical and modeling problems.

Perform deep exploratory data analysis (EDA) on complex, noisy, and often incomplete datasets; validate data quality, uncover patterns, and frame hypotheses.

Design and implement features, select appropriate modeling approaches (e.g., regression, tree-based models, time series, clustering), and benchmark alternatives.

Write clean, well-structured Python code and notebooks for interactive analysis, experimentation, and result communication.

Build end‑to‑end data and modeling pipelines (data extraction, preprocessing, feature generation, model training, evaluation, and inference) that are modular, testable, and reproducible.

Collaborate with engineering teams to productionize models on the Aera platform, including packaging, versioning, monitoring, and validation.

Communicate findings and recommendations clearly to both technical and non‑technical stakeholders using visualizations, written narratives, and live walkthroughs.

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.

1–3 years of experience in data science, data analysis, or ML engineering, or equivalent strong project/internship experience.

Strong proficiency in Python for data analysis: pandas, NumPy, and common scientific/ML libraries (e.g., scikit‑learn; familiarity with TensorFlow/PyTorch is a plus).

Solid experience with EDA and statistical analysis; comfortable working in notebooks to iteratively explore data and build intuition.

Good working knowledge of SQL and data manipulation; able to join, aggregate, and transform data from multiple sources.

Experience structuring code into reusable components, applying basic software engineering practices (version control, testing, documentation).

Strong analytical thinking and problem‑solving skills, with a bias for clear, data‑backed decision making.

Exposure to production ML/analytics systems (scheduling, APIs, CI/CD, monitoring).

Experience with cloud platforms (AWS, GCP, or Azure) and distributed data frameworks (e.g., Spark).

Domain experience in supply chain, logistics, or operations.