Chief Data Strategist

2 days ago

Thrissur, Kerala, India beBeeData Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Overview

The Fraud Risk Manager contributes as a fraud SME and will support data, machine learning models and generative AI technology and reporting analysts of Investigation & Loss Mitigation, as well as operate as an individual contributor on strategic initiatives.

This role requires maintaining effectiveness of investigation tools and technologies, data processes and reporting that delivers robust and timely data insights to key stakeholders.

The successful candidate thrives in a fast-paced, high-performance team environment where creativity and common sense result in great solutions.

  • Hands-on experience in analytics and data science.
  • Model development and exposure to fraud detection.
  • Good understanding of large language models and other new techniques.
  • Data deep dive, insight generation, dashboard reporting and presentation skill.
  • End-to-end project delivery and stakeholder management.
Key Responsibilities

Data Analysis and Business Insights

  • Analyzing large amounts of information to discover trends and patterns.
  • Combining models through ensemble modeling.
  • Presenting information using data visualization techniques.
  • Proposing solutions and strategies to business challenges.

System Validations

  • Monitoring fraud trends, investigating emerging threats and quickly developing and deploying new strategies to manage those threats.
  • Building predictive models and machine-learning algorithms.
  • Optimizing new and current database systems.
  • Reviewing existing control environments and assessing their effectiveness against known fraud schemes.

People Management

  • Working collaboratively with a diverse team of operational agents, fraud risk managers, data analysts/scientists, business insight analysts and subject matter experts.
  • Supporting team members to locate and define new process improvement opportunities.
Educational Qualifications
  1. M.Sc in Statistics, Economics or Engineering degree or professional education in Data Science.
  2. Good knowledge of Python, R, SQL, MS Excel and SQL database & coding.

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