Senior Data Scientist

Role Overview

• Design, develop, and optimize machine learning models for demand forecasting, inventory optimization, and pricing analytics
• Perform exploratory data analysis (EDA), model diagnostics, and data quality assessments
• Engineer advanced features including promotions, seasonality, holidays, stockouts, lag variables, external signals, and signal processing techniques
• Design and execute model experiments, hypothesis testing, oracle testing, and statistical evaluations
• Evaluate and benchmark forecasting approaches including LightGBM, Random Forest, Gradient Boosting, Deep Learning (PyTorch), DeepAR, ARIMA/SARIMA, Prophet, ensemble methods, and Croston/TSB
• Own the full model lifecycle from development and backtesting to deployment, monitoring, drift detection, and retraining
• Design and implement LLM-powered applications using RAG, fine-tuning, prompt engineering, structured outputs, and vector databases
• Build AI systems that securely interact with enterprise data through governed APIs
• Evaluate and integrate commercial and open-source foundation models into production
• Develop explainability and transparency mechanisms for enterprise AI solutions
• Design and develop autonomous AI agents with multi-step reasoning and tool use
• Develop integrations using Model Context Protocol (MCP) and tool-calling architectures for ERP data access
• Implement human-in-the-loop (HITL) workflows, role-based security, and approval mechanisms
• Establish standards for reliability, traceability, and auditability of agentic systems
• Design Semantic Read APIs connecting AI models to ERP data securely and reliably
• Build scalable batch inference and feature pipelines on AWS SageMaker and Azure ML
• Contribute to CI/CD automation, model validation, and deployment pipelines
• Collaborate with Data Engineering and MLOps teams on Dockerized deployments, APIs, monitoring, and scalable inference
• Define standards for feature stores, data pipelines, and model versioning
• Partner with IT and Security teams to ensure compliant AI deployments
• Develop and deploy computer vision solutions for product classification, visual merchandising, and image-based retail applications
• Integrate multimodal vision capabilities into forecasting and AI agent workflows
• Evaluate and apply modern computer vision architectures to production use cases
• Own and maintain production AI models and pipelines while driving continuous optimization
• Balance maintenance of existing solutions with development of new AI capabilities
• Identify and remediate technical debt, performance bottlenecks, and scalability issues
• Serve as the technical owner for AI model performance and production incident resolution
• Maintain comprehensive documentation for models, pipelines, and experiments
• Ensure reproducibility through experiment tracking, Git version control, and model lineage
• Share knowledge through code reviews, technical documentation, and internal presentations
• Contribute to R&D documentation and formal technical specifications
• Independently scope, design, and deliver AI solutions with minimal supervision
• Mentor team members through code reviews and architectural guidance
• Translate business problems into robust AI solutions and communicate results to technical and business stakeholders
• Apply responsible AI principles, including fairness, transparency, and model risk management

Requirements

• PhD in Artificial Intelligence, Data Science, Computer Science, or a related quantitative field strongly preferred
• Minimum 5 years of industry experience in applied data science (excluding academic research, internships, and research positions)
• Proven experience deploying and maintaining production-scale ML systems
• Hands-on expertise with LLMs and Generative AI, including RAG, prompt engineering, structured outputs, and evaluation frameworks
• Experience building agentic AI systems with tool use, orchestration, and multi-step reasoning
• Working knowledge of MCP or comparable agent orchestration frameworks
• Experience developing and deploying computer vision models
• Demonstrated ability to independently lead complex technical initiatives
• Experience in commercial demand forecasting or time series modelling is a strong asset
• Experience with signal processing techniques is an asset
• Experience with ERP systems, retail data models, or supply chain data is an asset

Tech Stack

• AWS
• Azure
• ERP
• Oracle
• PyTorch

Benefits

• Health coverage (medical, dental, disability, and life insurance)
• Wellness program (gym membership reimbursement)
• Professional growth (training platforms, career development fee subsidy, etc.)
• Company events
• Referral program
• Flexible schedule