Hiring || Risk Data Scientist (LeadManager) Bangalore

Bangalore - India

Monthly Salary: Not Disclosed

Posted on: 3 hours ago

Vacancies: 1 Vacancy

Job Summary

Summary:

The Risk Data Scientist plays a critical role within the BSA/AML/OFAC Model Development and Monitoring Team driving the design development and ongoing evaluation of advanced transaction monitoring and screening models. This position is central to enhancing the accuracy and efficiency of fraud and risk detection systems particularly in high-volume banking environments. The ideal candidate will leverage strong expertise in machine learning statistical modeling and data analytics to reduce false positives improve model performance and support compliance with regulatory standards. The role requires a blend of technical excellence analytical rigor and leadership with responsibilities spanning model lifecycle management cross-functional collaboration and mentorship of junior analysts. Based in Bangalore this is a full-time in-office role requiring a deep understanding of risk analytics data governance and regulatory frameworks.

Responsibilities:

Design and develop transaction monitoring scenarios to detect suspicious activities.
Enhance scenario segmentation using advanced techniques such as clustering and pattern recognition.
Conduct periodic tuning of threshold parameters through systematic sample analysis and evaluation.
Build post-processing models using rare event logistic regression and machine learning to minimize false positives.
Research and implement fuzzy logic-based algorithms for OFAC sanction screening and related processes.
Develop NLP-driven models to improve the precision of screening outputs and reduce false alerts.
Lead the implementation of models into production environments with robust version control and deployment practices.
Establish and execute ongoing monitoring plans to assess model performance and compliance.
Maintain comprehensive documentation of model development deployment and validation processes.
Collaborate with model validation teams to ensure adherence to internal and external standards.
Deliver timely ad hoc analyses in response to business or regulatory requests.
Prepare and present actionable insights and strategic recommendations to senior stakeholders.
Uphold data accuracy governance and transparency across all analytical workflows.
Mentor and guide 34 junior analysts including task assignment quality review and professional development.

Requirements

Bachelors degree in Statistics Data Science Operations Research Industrial Engineering Mathematics or Physics with 6 years of relevant experience.
Masters degree in a related field with 4 years of relevant experience.
PhD in Statistics Operations Research Industrial Engineering Mathematics or Physics with 2 years of relevant experience.
68 years of experience for Lead; 9 years for Manager roles.
Proven experience in risk and fraud analytics with a banking or financial services background preferred.
Expertise in SQL and Python for data analysis modeling and automation.
Strong foundation in machine learning classification models and statistical modeling.
Experience with the CDSW platform and software development lifecycle practices.
Familiarity with web scraping and advanced data sourcing techniques.
Demonstrated ability to translate complex analytical results into clear strategic insights for non-technical audiences.
Exceptional communication coordination and mentorship skills.

Required Skills:

Summary: The Risk Data Scientist plays a critical role within the BSA/AML/OFAC Model Development and Monitoring Team driving the design development and ongoing evaluation of advanced transaction monitoring and screening models. This position is central to enhancing the accuracy and efficiency of fraud and risk detection systems particularly in high-volume banking environments. The ideal candidate will leverage strong expertise in machine learning statistical modeling and data analytics to reduce false positives improve model performance and support compliance with regulatory standards. The role requires a blend of technical excellence analytical rigor and leadership with responsibilities spanning model lifecycle management cross-functional collaboration and mentorship of junior analysts. Based in Bangalore this is a full-time in-office role requiring a deep understanding of risk analytics data governance and regulatory frameworks. Responsibilities: Design and develop transaction monitoring scenarios to detect suspicious activities. Enhance scenario segmentation using advanced techniques such as clustering and pattern recognition. Conduct periodic tuning of threshold parameters through systematic sample analysis and evaluation. Build post-processing models using rare event logistic regression and machine learning to minimize false positives. Research and implement fuzzy logic-based algorithms for OFAC sanction screening and related processes. Develop NLP-driven models to improve the precision of screening outputs and reduce false alerts. Lead the implementation of models into production environments with robust version control and deployment practices. Establish and execute ongoing monitoring plans to assess model performance and compliance. Maintain comprehensive documentation of model development deployment and validation processes. Collaborate with model validation teams to ensure adherence to internal and external standards. Deliver timely ad hoc analyses in response to business or regulatory requests. Prepare and present actionable insights and strategic recommendations to senior stakeholders. Uphold data accuracy governance and transparency across all analytical workflows. Mentor and guide 34 junior analysts including task assignment quality review and professional development. Requirements Bachelors degree in Statistics Data Science Operations Research Industrial Engineering Mathematics or Physics with 6 years of relevant experience. Masters degree in a related field with 4 years of relevant experience. PhD in Statistics Operations Research Industrial Engineering Mathematics or Physics with 2 years of relevant experience. 68 years of experience for Lead; 9 years for Manager roles. Proven experience in risk and fraud analytics with a banking or financial services background preferred. Expertise in SQL and Python for data analysis modeling and automation. Strong foundation in machine learning classification models and statistical modeling. Experience with the CDSW platform and software development lifecycle practices. Familiarity with web scraping and advanced data sourcing techniques. Demonstrated ability to translate complex analytical results into clear strategic insights for non-technical audiences. Exceptional communication coordination and mentorship skills.

Required Education:

Graduate

Summary:

Responsibilities:

Design and develop transaction monitoring scenarios to detect suspicious activities.
Enhance scenario segmentation using advanced techniques such as clustering and pattern recognition.
Conduct periodic tuning of threshold parameters through systematic sample analysis and evaluation.
Build post-processing models using rare event logistic regression and machine learning to minimize false positives.
Research and implement fuzzy logic-based algorithms for OFAC sanction screening and related processes.
Develop NLP-driven models to improve the precision of screening outputs and reduce false alerts.
Lead the implementation of models into production environments with robust version control and deployment practices.
Establish and execute ongoing monitoring plans to assess model performance and compliance.
Maintain comprehensive documentation of model development deployment and validation processes.
Collaborate with model validation teams to ensure adherence to internal and external standards.
Deliver timely ad hoc analyses in response to business or regulatory requests.
Prepare and present actionable insights and strategic recommendations to senior stakeholders.
Uphold data accuracy governance and transparency across all analytical workflows.
Mentor and guide 34 junior analysts including task assignment quality review and professional development.

Requirements

Bachelors degree in Statistics Data Science Operations Research Industrial Engineering Mathematics or Physics with 6 years of relevant experience.
Masters degree in a related field with 4 years of relevant experience.
PhD in Statistics Operations Research Industrial Engineering Mathematics or Physics with 2 years of relevant experience.
68 years of experience for Lead; 9 years for Manager roles.
Proven experience in risk and fraud analytics with a banking or financial services background preferred.
Expertise in SQL and Python for data analysis modeling and automation.
Strong foundation in machine learning classification models and statistical modeling.
Experience with the CDSW platform and software development lifecycle practices.
Familiarity with web scraping and advanced data sourcing techniques.
Demonstrated ability to translate complex analytical results into clear strategic insights for non-technical audiences.
Exceptional communication coordination and mentorship skills.