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Location: Pune, India
Department: R&D
Aera Technology is a pioneer in the growing category of Decision Intelligence Platforms and a Leader in theGartner® Magic Quadrant™ for 2026– the technology to digitize, augment, and automate decision-making processes with AI and machine learning. Through our AI decision automation platform, Aera Decision Cloud™, we are helping the best-known brands in the world make smarter, faster decisions.
Privately-held and VC-funded, we have a global team of over 400 Aeranauts – and we’re growing. We deliver Decision Intelligence innovation and services that enable enterprises to automate and scale decision making with accuracy and speed. We continue to be the trusted choice of market leaders for our proven ability to generate value and unlock opportunities that were previously unattainable.
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.
Most IT Jobs posts in Pune ask for 3–6 years of experience (44% of 1038 that say).
From live job posts on CoreHRx, updated 12 Nov 2026. Skill figures count posts that list their skills; salary figures only posts that show a salary.
The post does not state the pay. As an estimate, comparable posts on CoreHRx advertise ₹16L – ₹26L a year (typical for IT Jobs in Pune, 53 posts).
The post asks for 1–3 Yrs experience.
In Pune (hybrid).
It is a full time job.
Interview preparation · 60 questions
This guide is for Indian job seekers preparing for SQL rounds in data analyst, data engineer, backend developer and database roles, from freshers to experienced professionals. It has 60 questions in three levels: easy covers joins, grouping and NULLs; medium covers window functions, CTEs, indexes and isolation levels; hard covers locking, MVCC, query tuning and classic puzzles such as login streaks and medians. Answers use ANSI SQL, with MySQL 8 and PostgreSQL notes where the two differ. Write each query against your own sample tables first, then compare your result with the sample answer.
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