Associate Data Scientist – Modeling, Analytics & Pipelines

Team: Data Science & Machine Learning

Location: Pune, India

Commitment: Full-time

Workplace Type: hybrid

Responsibilities

• 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.

About You

• 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.

 

Nice to have

• 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.