Lead AI/ML solution design, development, deployment, and lifecycle management for scalable, production‑ready models.
Responsibilities
Execute end‑to‑end development of AI/ML systems
Provide architectural direction for ML platforms
Lead evaluation and integration of AI technologies
Collaborate with product and domain teams on ML roadmaps
Oversee ML model lifecycle management
Requirements
Bachelor’s or master’s degree in computer science, Data Science, Engineering, or a related technical field; master’s degree in data science is preferable
Lead end-to-end design, development, deployment, and adoption of scalable data science and AI solutions across multiple business domains.
Responsibilities
Lead the end-to-end lifecycle of advanced analytics, machine learning, Generative AI, and Agentic AI solutions
Translate ambiguous business problems into well-defined data science hypotheses, solution designs, success metrics, and scalable implementation roadmaps
Develop and maintain advanced predictive/forecasting models for sales, revenue, expense management, and cash-flow forecasting
Design, architect, train, and deploy advanced machine-learning frameworks and traditional time-series models
Own model validation, explainability, reliability, documentation, monitoring, and governance
Requirements
Bachelor’s or Master’s degree in computer science, Data Science, Information Technology, or a related technical discipline
Nice to have: experience in healthcare, commercial, supply chain, or financial analytics environments
Nice to have: good understanding of statistical forecasting methodologies
Senior Data Scientist to lead end-to-end design, development, deployment, and adoption of scalable data science and AI solutions across multiple business domains.
Responsibilities
Lead the end-to-end lifecycle of advanced analytics, machine learning, Generative AI, and Agentic AI solutions
Translate ambiguous business problems into well-defined data science hypotheses, solution designs, success metrics, and scalable implementation roadmaps
Develop and maintain advanced predictive/forecasting models for sales, revenue, expense management, and cash-flow forecasting
Design, architect, train, and deploy advanced machine-learning frameworks and traditional time-series models
Own model validation, explainability, reliability, documentation, monitoring, and governance
Requirements
Bachelor’s or Master’s degree in computer science, Data Science, Information Technology, or a related technical discipline
Nice to have: experience in healthcare, commercial, supply chain, or financial analytics environments
Nice to have: statistical forecasting methodologies
Build and productionize AI-driven demand forecasting, pricing, and optimization models.
Responsibilities
Develop, validate, and iterate on demand forecasting, pricing, and optimization models using large-scale, high-velocity market data.
Explore and analyze data from diverse internal and external sources to uncover signals, define features, and identify opportunities to improve model accuracy and business impact.
Design and run offline experiments, backtests, and online evaluations to measure model performance and profit uplift.
Write production-grade Python code and work with ML and data engineers to productionize models, ensuring they are reliable, observable, and cost-efficient.
Monitor deployed models for drift, accuracy, and data quality issues, and troubleshoot and fix problems quickly.
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