Key Responsibilities :

- Develop, implement, and optimize machine learning models to solve real-world business problems such as customer segmentation, demand forecasting, fraud detection, and recommendation systems.

- Write clean and efficient code in Python to build, test, and deploy analytical solutions.

- Utilize advanced statistical techniques and data modeling methodologies to analyze large-scale structured and unstructured datasets.

- Design and develop interactive dashboards and visualizations using tools such as Tableau, Power BI, or Matplotlib for data storytelling and stakeholder communication.

- Collaborate with data engineers to build and maintain scalable data pipelines using big data platforms such as Hadoop, Apache Spark, or Apache Flink.

- Work closely with DevOps teams to containerize and deploy ML models and data workflows using Docker, Kubernetes, and other deployment tools.

- Leverage cloud platform in GCP for scalable storage, computing, and model deployment.

- Present analytical findings and recommendations to technical and non-technical stakeholders across the organization.

Required Skills & Qualifications :

- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.

- Proven experience (4+ years) in a Data Scientist or similar role.

- Strong proficiency in at least one programming language - Python

- Hands-on experience with machine learning frameworks and model deployment tools.

- Solid understanding of statistical analysis, hypothesis testing, regression modeling, and other data science techniques.

- Expertise in data visualization tools - Tableau, Power BI, or Matplotlib.

- Familiarity with big data ecosystems including Hadoop, Spark, and/or Flink.

- Working knowledge of containerization and orchestration tools - Docker, Kubernetes.

- Experience working on cloud platform in GCP.

Preferred Qualifications (Good to Have) :

- Experience with CI/CD pipelines for ML model deployment.

- Exposure to Natural Language Processing (NLP) or Deep Learning frameworks (TensorFlow, PyTorch).

- Knowledge of feature engineering and model interpretability tools like SHAP, LIME.