Key Responsibilities :

- Lead end-to-end execution of data science and analytics projects, including problem definition, model development, validation, deployment, and monitoring.

- Perform data analysis, feature engineering, and data preparation using structured and alternative data sources.

- Develop scalable frameworks for deploying and consuming machine learning models in production.

- Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.

- Monitor model performance and recommend enhancements for continuous improvement.

- Mentor and guide junior data scientists and support knowledge-sharing initiatives.

- Ensure adherence to best practices in model development, documentation, and governance.

Required Skills & Experience :

- 4 - 8 years of experience.

- Strong understanding of Machine Learning algorithms, predictive analytics, and statistical modeling.

- Hands-on experience in end-to-end model development, deployment, and implementation.

- Strong proficiency in Python for data analysis and machine learning.

- Experience in feature engineering using traditional and alternate data sources.

- Strong knowledge of data preprocessing, model evaluation, and performance optimization.

- Excellent analytical, problem-solving, and communication skills.

- Experience working directly with business stakeholders and cross-functional teams.

Preferred Qualifications :

- Bachelor's or Master's degree in :

1. Computer Science

2. Data Science

3. Engineering

4. Or any other quantitative discipline.

- Experience with SQL, cloud platforms (AWS, Azure, or GCP), and MLOps tools is an advantage.

- Knowledge of credit risk, fraud detection, customer analytics, collections, or marketing analytics in BFSI is preferred.

Key Competencies :

- Machine Learning & Statistical Modeling

- Python & Data Science

- Feature Engineering

- Predictive Modeling

- Model Deployment & Productionization

- Mentoring & Team Collaboration