Aera Technology
From Aera Technology's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
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.
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.
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).
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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