Role Overview :

As a Machine Learning Engineer specializing in Demand Forecasting, you will be at the forefront of building sophisticated predictive models that drive critical supply chain and inventory decisions. You will work closely with cross-functional teams, including product managers, data engineers, and business stakeholders, to translate complex market signals into actionable insights.

By leveraging advanced statistical techniques and deep learning, you will directly influence business outcomes, optimizing stock levels, reducing operational costs, and enhancing the overall customer experience through precise demand anticipation. This role is based in Gurugram and requires a collaborative mindset to bridge the gap between technical model performance and real-world business utility.

Key Responsibilities :

- Architect and deploy scalable time-series forecasting models to predict consumer demand across diverse product categories, ensuring high accuracy for inventory planning.

- Collaborate with product teams to integrate predictive insights into the UX, ensuring that end-users receive intuitive and actionable data visualizations.

- Implement robust MLOps pipelines to automate model training, versioning, and monitoring, ensuring consistent performance in a production environment.

- Analyze large-scale datasets using SQL to identify trends, seasonality, and anomalies that inform feature engineering and model refinement.

- Partner with business stakeholders to translate strategic objectives into technical requirements, ensuring that machine learning solutions align with organizational growth targets.

Required Skillset :

- Demonstrated expertise in building time-series forecasting solutions using Python, with a strong command over libraries such as scikit-learn, LightGBM, and PyTorch.

- Proven ability to manage the end-to-end machine learning lifecycle, including data extraction via SQL, model development, and MLOps deployment.

- Strong analytical mindset with the ability to communicate complex technical concepts to non-technical stakeholders, fostering a culture of data-driven decision-making.

- A background in a quantitative field (Engineering, Mathematics, or Statistics) is preferred, reflecting a solid foundation in algorithmic thinking.

- Adaptability to a hybrid work environment in Gurugram, with the ability to thrive in a fast-paced, collaborative team setting.

- Minimum of 3 - 7 years of professional experience in developing and deploying machine learning models in a production-grade environment.