Partner with product managers, engineers and other stakeholders to understand business problems and identify opportunities for machine learning applications to optimize item lifecycle performance.
Perform exploratory data analysis and statistical modelling to extract actionable insights from large and complex datasets
Build and maintain robust data pipelines to process, clean and transform data from diverse sources ( e.g. SQL datasets, APIs, flat files)
Design, develop and implement scalable and production-ready machine learning models / optimization algorithms for areas such as demand forecasting, assortment, and pricing optimization.
Implement robust evaluation and monitoring to validate the performance and reliability of machine learning models
Communicate complex findings, insights and trade-offs to technical and non-technical stakeholders

Bachelor's, Master's, or PhD in Statistics, Data Science, Computer Science, Engineering, Operations Research, or a related technical field; or Equivalent related professional experience.
Minimum 1 years hands-on experience in Data Science or Machine Learning roles
Minimum 1 years of professional SQL experience, performing advanced queries and optimization techniques
Proficient coding skills in Python, with experience writing clean, maintainable, and optimized ML code
Experience applying statistical and machine learning algorithms, such as regression, decision trees, clustering, neural networks, survival analysis, along with model evaluation techniques
A passion for solving complex problems with creative approaches.
Strong communication and collaboration skills, with the ability to work both independently and as part of a team

Data Scientist – Merchandising, Inventory, Machine Learning

Key skills