Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
• AND 1+ year(s) data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
• AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
• AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• 12+ years of experience shipping AI / ML models and solutions
• 3+ years of proven experience in managing and leading AI / ML

Preferred Qualifications:

• Excellent collaboration and communication skills to work effectively with cross-functional teams
• Strong problem-solving abilities and a strategic mindset to drive innovation and achieve business goals
• Experience with AI product integration and deployment in real-world scenarios
• Familiarity with Microsoft technologies and platforms like Azure ML Ops, Azure AI Studio, Copilot Studio etc is a big plus.
• Prior experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel).
• Prior experience in time series forecasting.
• Prior experience with typical data management systems and tools such as SQL.
• Knowledge and ability to work within a large-scale computing or big data context, and hands-on experience with Hadoop, Spark, Synapse/DataBricks or similar.
• Excellent analytical skills; ability to understand business needs and translate them into technical solutions, including analysis specifications and models.
• Advanced degree (Ph.D. or equivalent) in Computer Science, Engineering, or a related field is preferred.
• PhD in Statistics, Applied Mathematics, Applied Economics, Computer Science or Engineering, Data Science, Operations Research or similar applied quantitative field.
• Experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel) with skill level at or near fluency.
• Experience with deep learning models (e.g., tensorflow, PyTorch, CNTK) and solid knowledge of theory and practice.
• Practical and professional experience contributing to and maintaining a large code base with code versioning systems such as Git.
• Knowledge of supply chain models, operations research techniques, optimization modelling and solvers.