Aera Technology is a pioneer in the growing category of Decision Intelligence Platforms and a Leader in the Gartner® 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.

The need for decision intelligence has never been greater. The digitization of the economy has introduced a paradox: Companies today are awash in data, yet the velocity, volume, and complexity of that information is outpacing their ability to make decisions with the speed and precision that today’s business environment demands. Decision intelligence represents the critical shift — moving from people making decisions with machine support to machines making decisions guided by people. As the digital economy accelerates into an era defined by agentic AI, this shift has become a competitive necessity, enabling organizations to act faster, adapt with confidence, and unlock value once beyond reach.

Aera Technology is at the forefront of this agentic revolution. Recognized by IDC as a leader in decision intelligence and cited by Gartner in its Market Guide for Decision Intelligence, we’re the trusted choice of global market leaders. Built on a comprehensive and composable platform, Aera empowers organizations to optimize and automate all types of decisions, across every business area.

We are looking for an Associate Data Scientist with exceptional strength and deep interest in Operations Research and mathematical optimization. In this role, you will turn complex planning, pricing, and supply chain problems into well-structured optimization models and help design algorithms that run at scale in real customer environments.

• Work with real-world business data to formulate optimization problems in areas such as supply chain, inventory, production planning, and pricing.

• Design, implement, test, and refine linear, integer, and mixed-integer programming models, along with heuristic or meta-heuristic approaches where needed.

• Prototype and compare alternative formulations and solution strategies, balancing optimality, robustness, and runtime performance.

• Use commercial and open-source solvers (e.g., Gurobi, CPLEX, COIN-OR) effectively, including model diagnostics, tuning, and performance analysis.

• Collaborate with solution delivery and data engineering teams to integrate optimization models into end‑to‑end decision workflows.

• Engage with customers and internal stakeholders to understand requirements, explain model behavior and trade-offs, and iterate on designs.

• Bachelor’s or Master’s degree in Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, or a related quantitative field.

• 1-3 of experience in optimization / OR, including internships, academic projects, or industry experience; outstanding fresh graduates are encouraged to apply.

• Strong foundations in linear algebra, probability, optimization theory, and algorithms, with clear intuition for LP/MIP modeling.

• Hands-on experience formulating and solving optimization models using at least one solver (Gurobi, CPLEX, COIN-OR, etc.) and one programming language (preferably Python).

• Demonstrated passion for OR: coursework, theses, competitions, or open-source contributions.

• Ability to reason from first principles, question assumptions, and propose alternative formulations when the first approach does not work.

• Comfortable working in a fast-paced environment, taking ownership, and learning new problem domains quickly.

• Exposure to supply chain, logistics, or pricing optimization problems.

• Experience with large-scale data handling (SQL, Spark, or similar) to prepare input data for optimization runs.

• Familiarity with building small services or scripts to operationalize optimization runs (e.g., scheduling, APIs, or batch jobs).