Own end-to-end modeling workflows & build production-ready ML pipelines for Aera's Decision Intelligence platform. Work on complex datasets, apply statistical/ML methods, and collaborate with engineering to deploy scalable solutions.

About the Company Aera Technology is a pioneer in the rapidly growing category of Decision Intelligence Platforms, recognized as a Leader in the Gartner® Magic Quadrant™ for 2026. The company's AI decision automation platform, Aera Decision Cloud™, helps the world's best-known brands digitize, augment, and automate their decision-making processes using AI and machine learning. Established in 2017 and headquartered in Mountain View, California, Aera is a Series D start-up with a global footprint spanning Mountain View and San Francisco (USA), Bucharest and Cluj-Napoca (Romania), Paris (France), Munich (Germany), London (UK), Pune (India), and Sydney (Australia). Aera is at the forefront of the agentic AI revolution, enabling organizations to act faster, adapt with confidence, and unlock value once beyond reach. As an equal opportunity employer, Aera is committed to fostering an inclusive environment for all Aeranauts.

Role Overview As an Associate Data Scientist, you will be a key contributor to Aera's mission of empowering organizations to make smarter, faster decisions. You will own the full modeling workflow—from exploratory analysis and feature engineering to building reproducible, production-ready pipelines. Your work will directly impact how the company's enterprise customers optimize and automate decisions across supply chain, logistics, and operations. This role is ideal for a data scientist who thrives on ambiguity, is passionate about turning raw data into actionable insights, and has the technical skills to build robust, scalable models that deliver real business value.

Key Responsibilities • Partner with product managers and domain experts to translate ambiguous business questions into clear, data-driven modeling problems. • Perform deep exploratory data analysis (EDA) on complex, noisy, and often incomplete datasets, validating data quality and uncovering hidden patterns. • Design and engineer features, select appropriate modeling approaches (regression, tree-based models, time series, clustering), and benchmark alternatives to ensure optimal performance. • Write clean, well-structured Python code and interactive notebooks for experimentation, analysis, and result communication. • Build end-to-end data and modeling pipelines—including data extraction, preprocessing, feature generation, model training, evaluation, and inference—that are modular, testable, and reproducible. • Collaborate closely with engineering teams to productionize models on the Aera platform, focusing on packaging, versioning, monitoring, and validation. • Communicate findings and recommendations clearly to both technical and non-technical stakeholders using visualizations, written narratives, and live walkthroughs.

Tech Stack • Programming Languages: Python (primary) • Data Manipulation & Analysis: pandas, NumPy, SQL • Machine Learning & Statistics: scikit-learn, statistical analysis, EDA, feature engineering • Deep Learning (Preferred): TensorFlow, PyTorch (nice-to-have) • Big Data & Cloud (Preferred): Spark, AWS/GCP/Azure • Software Engineering: Git (version control), testing frameworks, documentation practices • Other: Notebooks (Jupyter), modular code structuring, CI/CD exposure (nice-to-have)

In the context of a comprehensive data science role, key qualifications include a Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field, along with 1-3 years of relevant experience (or equivalent strong project/internship exposure). Ideal candidates will demonstrate proficiency in Python for data analysis, specifically with pandas, NumPy, and scientific/ML libraries such as scikit-learn, with TensorFlow/PyTorch familiarity as a plus. Solid experience with exploratory data analysis (EDA) and statistical analysis is essential, as is a strong working knowledge of SQL for data manipulation and aggregation. Experience structuring code into reusable components and applying basic software engineering practices (version control, testing, documentation) is required. Strong analytical thinking and problem-solving skills with a bias for data-backed decision-making are critical.

Competitive salary and company stock options. Comprehensive Group Medical Insurance, Term Insurance, and Accidental Insurance. Paid time off, maternity leave, and flexible working environment to support work-life balance. Unlimited access to online professional courses for professional and personal development, including people manager development programs. Fully-stocked kitchen with snacks and beverages when working from the office.