Key Responsibilities :

- Apply statistical analysis, machine learning, and predictive modeling to develop business-driven solutions.

- Build and optimize models for classification, regression, and causal inference.

- Work with supervised learning methods including XGBoost, Ridge, ElasticNet, and Neural Networks.

- Design and implement predictive analytics models for real-world use cases.

- Apply causal inference and optimization techniques to derive actionable insights.

- Collaborate with cross-functional teams (data engineers, business analysts, stakeholders) to integrate models into production systems.

- Perform model validation, performance evaluation, and tuning to ensure scalability and accuracy.

- Communicate findings and insights effectively to both technical and non-technical stakeholders.

Skills & Expertise Required :

- Strong knowledge in Machine Learning & Predictive Analytics.

- Hands-on experience with models : XGBoost, Ridge Regression, ElasticNet, Neural Networks.

- Expertise in causal inference and optimization methodologies.

- Proficiency in Python/R for statistical modeling and data manipulation.

- Experience with SQL and working with large datasets.

- Solid understanding of statistical concepts, data visualization, and feature engineering.

Preferred Skills :

- Exposure to cloud-based ML platforms (AWS, Azure, GCP).

- Knowledge of MLOps and deployment of models into production.

- Familiarity with Big Data tools (Spark, Hadoop).