Associate Data Scientist – Modeling, Analytics & Pipelines

Aera Technology is the Decision Intelligence company. We deliver innovation and services that enable enterprises to operate sustainably, intelligently, and efficiently. Our platform, Aera Decision Cloud™, integrates with your existing systems to digitize, augment, and automate decisions in real time. Aera helps enterprises around the world transform decision making – delivering millions of recommendations that have resulted in significant revenue gains and cost savings for some of the world’s best-known brands.

• 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.

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