Senior Data Scientist

As an experienced data scientist, you will be responsible for leading the development of predictive models from exploration to full-scale production deployment. Your key responsibilities will include: - End-to-End Product Ownership: Taking charge of developing predictive models and ensuring their successful deployment in production environments. - Prediction: Building robust regression, time-series, and deep learning models to predict prices/values of various commodities like financial assets, oil, and apparel. - Model Optimization: Continuously fine-tuning models for accuracy, performance, and scalability using real-time data feedback. - ML Ops & Deployment: Collaborating with engineering teams to deploy and monitor models effectively. - Stakeholder Collaboration: Translating business problems into analytical frameworks by closely working with product, strategy, and business teams. - Data Strategy: Defining and managing pipelines and feature stores using structured and unstructured data sources. Qualifications required for this role include: - 8+ years of experience in data science with a focus on predictive analytics and machine learning. - Proven expertise in building and scaling ML models in production environments. - Deep knowledge of Python, SQL, and ML libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow/PyTorch. - Strong understanding of regression techniques, time-series forecasting, and feature engineering. - Preferably experience in domains like finance, commodities, retail pricing, or demand prediction. - Familiarity with cloud platforms like Azure, AWS, or GCP, Azure ML or Sagemaker, and tools such as Airflow, Docker, and MLflow. - Ability to define success metrics, conduct A/B tests, and iterate based on measurable KPIs. - Excellent communication and storytelling skills for effective interaction with technical and non-technical stakeholders.,