• Design, develop, and deploy demand forecasting models using traditional and machine learning techniques like ARIMA, XGBoost, and LSTM.

• Leverage Generative AI (GenAI) to enhance demand forecasting accuracy by simulating future demand scenarios and improving model predictions.

• Implement dynamic pricing models using advanced algorithms to optimize pricing strategies based on demand predictions, inventory levels, and competitor data.

• Analyze customer behavior by applying clustering algorithms and predicting customer lifetime value (CLV) with machine learning models to drive personalized pricing strategies.

• Monitor, evaluate, and fine-tune model performance using metrics such as MAE, RMSE, and Precision/Recall, continuously improving model accuracy and robustness.

• Apply MLOps practices, including version control, model deployment pipelines, and automated testing, to ensure seamless integration, monitoring, and scaling of models across environments.

• Collaborate with cross-functional teams, including marketing, product, and sales, to ensure alignment between forecasting models and business objectives.

• Deploy and scale forecasting and pricing models on Azure cloud platform, leveraging CI/CD pipelines and model monitoring tools for real-time performance tracking and proactive issue resolution.

Desired Candidate Profile

• Advanced machine learning model development and optimization skills.

• Expertise in Generative AI techniques for demand forecasting.

• Proficiency in MLOps practices and model deployment pipelines.

• Strong communication and collaboration abilities with cross-functional teams.

What Qualifies You For The Role:

• Master's degree in Statistics, Data Science, Machine Learning, Mathematics, Computer Science, or a related quantitative field.

• 10+ years of experience in data science, with at least 5+ years focused on demand forecasting, price optimization, and customer analytics.

• Strong hands-on experience with ARIMA, XGBoost, LSTM, and OR models.

• Proficiency in Python, SQL, Databricks, and cloud platforms (Azure, GCP).