Data Scientist – Fraud & Scams Detection

What impact will you make?

Every day, your work will make an impact that matters, while you thrive in a dynamic culture of inclusion, collaboration and high performance. As the undisputed leader in professional services, Deloitte is where you will find unrivaled opportunities to succeed and realize your full potential

Deloitte is where you will find unrivaled opportunities to succeed and realize your full potential.

The Team

Deloitte’s practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact-driven decision-making and generate an insight-driven advantage. Our practice addresses the continuum of opportunities in business intelligence & visualization, data management, performance management and next-generation analytics and technologies, including big data, cloud, cognitive and machine learning. Learn more about Analytics and Information Management Practice

Work you will do-

Role Purpose: - The Data Scientist – Fraud & Scams Detection will be responsible for designing, developing, and deploying advanced machine learning models to detect and mitigate fraud in digital and card transactions. This role supports the transition from a third-party solution to an internally developed and managed fraud detection system. The successful candidate will play a key role across the full model lifecycle—from early-stage analysis and feature engineering through to deployment and post-production monitoring working closely within an agile squad environment

Yrs of Exp -5 -8Yrs

Key Responsibilities

• Design and develop fraud and scams detection models tailored to digital and card transaction data.
• Replace existing third-party fraud detection solutions with internally developed models.
• Define and test different ML approaches suitable for the fraud domain.
• Lead feature engineering, feature selection, and hyperparameter tuning for model optimization.
• Collaborate with business stakeholders, engineers, and squad members from initial discovery to deployment.
• Articulate design choices, modeling approaches, and monitoring strategies clearly and effectively.
• Deploy models into production using modern MLOps practices.
• Maintain model accuracy, relevance, and compliance through ongoing monitoring and evaluation.

Key Performance Indicators (KPIs)

• Successful deployment of fraud detection models with measurable accuracy and performance.
• Reduction in false positives/negatives relative to the third-party benchmark.
• Timely delivery of model milestones aligned to project timelines.
• Evidence of collaboration and active contribution in agile squad setup.
• Quality and robustness of documentation and monitoring processes.

Essential Experience

• Proven experience in developing and deploying fraud detection or risk models in production environments.
• Experience with end-to-end model development, including:
• Algorithm selection
• Feature engineering and selection
• Model training and tuning
• Deployment and post-deployment monitoring
• Strong understanding of fraud patterns and digital transaction behaviors.
• Hands-on experience working within agile teams or squads on data science delivery.

Technical & Functional Competencies

• High proficiency in SQL, Python, PySpark
• Strong understanding of version control using Git
• Experience with Databricks
• Familiarity with MLOps frameworks and production workflows

Soft Skills & Attributes

• Analytical mindset with a strong problem-solving approach.
• Ability to articulate complex data science concepts and decisions to non-technical stakeholders.
• Proactive, self-driven, and accountable for delivering high-quality outcomes.
• Strong communication and collaboration skills.
• Comfortable working in fast-paced, evolving environments.

Qualification Requirements

• Bachelor's or postgraduate degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.

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