Job Purpose

This role leverage data analytics, statistical modeling, and machine learning techniques to develop AI-driven solutions that enhance banking operations, optimize customer experiences, and drive business insights.

Principle Responsibilities

• Develop and implement predictive and prescriptive ML models for key banking applications such as fraud detection, credit risk scoring, and customer segmentation.
• Perform exploratory data analysis (EDA) to uncover insights and inform model development.
• Work closely with business stakeholders to understand banking challenges and translate them into AI-driven solutions.
• Conduct feature engineering, selection, and dimensionality reduction to enhance model performance.
• Implement time-series forecasting, NLP, or deep learning techniques as needed for specific banking use cases.
• Use statistical analysis, hypothesis testing, and A/B testing to validate AI-driven recommendations.
• Collaborate with MLOps and engineering teams to deploy models into production with API integrations and cloud-based solutions.
• Monitor model performance in production, retraining models when necessary to handle data drift.

Qualifications Required

• 3+ years of experience in machine learning, statistical modeling, or data analysis.
• Proficiency in Python, SQL, and ML frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Strong understanding of statistics, probability, and data science principles.
• Experience with data visualization tools (Tableau, Power BI, Matplotlib, Seaborn) to communicate insights effectively.
• Familiarity with cloud-based ML solutions on AWS, Azure, or GCP.
• Strong problem-solving skills and ability to work with large datasets.
• Strong analytical and critical-thinking skills.
• Ability to translate business challenges into AI/ML solutions.
• Effective communication and data storytelling skills.
• High attention to detail and model accuracy.
• Ability to work both independently and as part of a team.

• Develop and implement predictive and prescriptive ML models for key banking applications such as fraud detection, credit risk scoring, and customer segmentation.
• Perform exploratory data analysis (EDA) to uncover insights and inform model development.
• Work closely with business stakeholders to understand banking challenges and translate them into AI-driven solutions.
• Conduct feature engineering, selection, and dimensionality reduction to enhance model performance.
• Implement time-series forecasting, NLP, or deep learning techniques as needed for specific banking use cases.
• Use statistical analysis, hypothesis testing, and A/B testing to validate AI-driven recommendations.
• Collaborate with MLOps and engineering teams to deploy models into production with API integrations and cloud-based solutions.
• Monitor model performance in production, retraining models when necessary to handle data drift.

• Proficiency in Python, SQL, and ML frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with data visualization tools (Tableau, Power BI, Matplotlib, Seaborn).
• Familiarity with cloud-based ML solutions on AWS, Azure, or GCP.
• Strong problem-solving skills.
• Strong analytical and critical-thinking skills.
• Effective communication and data storytelling skills.
• High attention to detail and model accuracy.

• 3+ years of experience in machine learning, statistical modeling, or data analysis.
• Strong understanding of statistics, probability, and data science principles.
• Ability to work with large datasets.
• Ability to translate business challenges into AI/ML solutions.
• Ability to work both independently and as part of a team.