Data Scientist – Machine Learning
ŌURA
Python
Job Level
Senior
Tech Stack
Python
About the role
- Lead data science workstreams and ensure alignment with business objectives and technical excellence
- Direct optimization-focused teams for asset, product, and placement optimization projects (planogram, assortment, allocation, whitespace/category)
- Collaborate with stakeholders to translate business challenges into mathematical optimization problems
- Implement solvers and express complex optimization models in solver constraint languages (Gurobi, Pyomo, etc.)
- Build and maintain backend Python code integrating optimization solvers for data manipulation, model orchestration, and automation
- Apply best practices and industry-standard frameworks in numerical optimization
- Contribute to recommender systems and other advanced analytics solutions as needed
- Stay abreast of emerging trends in optimization, machine learning, and retail analytics and champion adoption
- Mentor junior data scientists and foster a culture of continuous learning
- Work closely with product managers, engineers, and business stakeholders to deliver impactful solutions
Requirements
- PhD in Mathematics, Operations Research, Computer Science, or a related quantitative discipline (Master’s degree also considered)
- Deep expertise in formulating and solving numerical optimization problems (MIP, LP, etc.)
- Hands-on experience with optimization frameworks such as Gurobi, Pyomo, CPLEX, or similar
- Proficiency in Python for backend integration and model development
- Proven ability to translate business problems into mathematical models and implement end-to-end optimization solutions
- Excellent communication and stakeholder management skills
- Preferred: Experience in retail analytics (planogram optimization, assortment optimization, allocation optimization, whitespace/category optimization)
- Preferred: Familiarity with recommender systems and related machine learning techniques
- Preferred: Prior leadership experience in data science or optimization teams
- Preferred: Experience working in a global, cross-functional environment
