Sr Data Scientist

Key Responsibilities

• End-to-End Product Ownership: Lead the development of predictive models from exploration and prototype to full-scale production deployment.
• Prediction: Build robust regression and/or time-series and/or deep learning models to predict prices/values of financial assets, oil, apparel, and other commodities.
• Model Optimization: Continuously monitor and fine-tune models for accuracy, performance, and scalability using real-time data feedback.
• ML Ops & Deployment: Collaborate with engineering to ensure successful deployment and monitoring of models in production environments.
• Stakeholder Collaboration: Translate business problems into analytical frameworks, working closely with product, strategy, and business teams.
• Data Strategy: Define and manage pipelines and feature stores using structured and unstructured data sources.
  

Required Qualifications

• 8+ years of experience in data science, with a strong background in predictive analytics and machine learning.
• Proven experience building and scaling ML models in production environments (not just notebooks or PoCs).
• Deep expertise in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, LightGBM, TensorFlow/PyTorch).
• Strong knowledge of regression techniques, time-series forecasting, and feature engineering.
• Experience in domains such as finance, commodities, retail pricing, or demand prediction is highly preferred.
• Experience working with cloud platforms (Azure, AWS or GCP), Azure ML or Sagemaker, tools like Airflow, Docker, and MLflow.
• Ability to define success metrics, conduct A/B tests, and iterate based on measurable KPIs.
• Excellent communication and storytelling skills with both technical and non-technical stakeholders.