This role requires a seasoned professional who can drive the end-to-end lifecycle of machine learning models from data exploration and feature engineering to production deployment and ongoing model management.

The ideal candidate has a strong foundation in predictive analytics, hands-on experience building scalable ML solutions in production, and a knack for translating complex data insights into clear, actionable business recommendations.

Key Responsibilities :

- Design, develop, and deploy robust machine learning models and predictive analytics solutions to solve complex business challenges.

- Lead the full ML lifecycle: data acquisition, cleaning, feature engineering, model selection, training, evaluation, deployment, and monitoring.

- Collaborate closely with cross-functional teams including engineering, product management, and business stakeholders to align ML solutions with business objectives.

- Develop and maintain scalable, production-grade machine learning pipelines using tools such as Airflow, Docker, and MLflow.

- Build automated workflows for model retraining, validation, and deployment in cloud environments such as Azure ML, AWS SageMaker, or Google Cloud AI Platform.

- Utilize advanced statistical techniques including regression analysis, time-series forecasting, and anomaly detection to inform decision-making.

- Conduct rigorous experimentation including A/B testing to evaluate model performance and impact on key business metrics.

- Define and track success metrics and KPIs to continuously optimize models and strategies.

- Provide clear and compelling data-driven storytelling and presentations to both technical and non-technical audiences.

- Mentor junior data scientists and contribute to the development of best practices within the team.

- Stay current with emerging trends in machine learning, data science, and AI to continuously innovate and improve methodologies.

Required Qualifications :

- 8+ years of hands-on experience in data science, with demonstrated success building and scaling ML models beyond prototypes or research projects.

- Strong proficiency in Python and SQL, with expertise in ML libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch.

- Deep understanding of predictive modeling techniques, including regression, classification, time-series forecasting, and feature engineering.

- Experience working with large datasets and cloud platforms including Azure, AWS, or GCP; hands-on experience with managed ML services such as Azure ML or SageMaker.

- Practical knowledge of workflow orchestration tools like Apache Airflow and containerization using Docker.

- Expertise in experiment design and analysis, including A/B testing and statistical hypothesis testing.

- Ability to translate complex technical concepts into clear business insights and communicate effectively across teams.

- Experience in relevant domains such as finance, commodities trading, retail pricing, or demand forecasting is highly desirable.