The client is focused on becoming a data-driven digital leader, with the goal of delivering measurable value both internally and to their customers. To achieve this, they are investing in the development of advanced analytics products and services, integrating them into their business operations and customer offerings. This transformation requires new expertise and a range of specialized skills in analytics and technology.

The project aims to enhance the Straight Through Processing (STP) in the periodic Customer Due Diligence (CDD) review process. Depending on customer risk levels, periodic reviews occur every 1, 2, 3, 5, or 10 years. STP automates the decision-making by applying a rule-based system that determines whether a manual review is necessary. The goal is to boost STP efficiency using an AI model that predicts whether a manual check is required, based on historical review data.

The Data Scientist will be responsible for collecting, analyzing, and interpreting large datasets to build machine learning models that improve STP accuracy. The focus will be on machine learning, classification, statistical analysis, and predictive modeling, requiring skills in Python/Golang and tools like Spark/PySpark. The role involves collaborating with data engineers, extracting actionable insights, and communicating findings to business stakeholders.

Key skills:

• Expertise in machine learning, classification, and explainability of ML models
• Proficiency in Python/Golang
• Experience with large datasets and data engineering tools (e.g., Spark)
• Ability to translate data analysis into business insights

Additional Skills (Nice to Have):

• Experience in integrating data science solutions into products
• Cloud experience in platforms like Azure, GCP, or AWS
• Knowledge of monitoring and improving model performance in production environments