About the role
Responsibilities:
- You'll architect intelligent systems that sense demand shifts in real time and recommend actions before a human even asks.
- Agentic AI pipelines that go beyond prediction systems that reason, decide, and adapt autonomously.
- Advanced demand forecasting and sensing models using attribute-based and cohort-based approaches.
- Signal integration frameworks that fuse external data and real-time market inputs.
- Feature engineering at scale: lag structures, rolling aggregations, and derived features.
- Model performance frameworks tied directly to measurable business outcomes.
- Translating complex model insights into clear, actionable language for clients and leadership.
Key Performance Indicators:
- Model accuracy, reliability, and real-world forecast performance.
- Speed and quality of model deployment to production pipelines.
- Business impact of AI-driven recommendations measured in inventory efficiency and revenue outcomes.
- Cross-functional collaboration effectiveness with product, engineering, and client teams.
Requirements:
- You bring a strong foundation in machine learning and a genuine curiosity about where AI is heading.
- 2-6 years of hands-on experience in ML, AI, or Data Science with a proven model-building track record.
- Python fluency: pandas, scikit-learn, XGBoost, and LightGBM are second nature to you.
- Deep understanding of time series forecasting and experience with large-scale transactional datasets.
- Ability to connect model outputs to real business impact and articulate the 'so what' clearly.
- Genuine curiosity about the power of AI and openness to emerging tools and paradigms, including agentic and generative AI.
Bonus Points:
- Experience in retail, supply chain, or inventory planning domains.
- Exposure to hierarchical forecasting, new product forecasting, or ensemble methods.
- Hands-on work with LLM-based agents or orchestration frameworks.