Senior Data Scientist Fraud Detection & AI Application

Role Overview: As a Data Scientist at PureSpectrum, you will be part of the Data Science & AI team in Hyderabad. Your primary focus will be on fraud detection and threat mitigation within the market research ecosystem. You will play a crucial role in architecting real-time anomaly detection systems, contributing to feature engineering, and engaging in distributed computing to ensure data quality and reliability. Key Responsibilities: - Architect Real-Time Risk & Fraud Systems: Design, train, and deploy high-throughput machine learning models for detecting identity spoofing, survey fraud, and bot nets. Extend these capabilities to cybersecurity risk scoring, threat intelligence, and vendor risk assessments. - Process large scale transaction data: Develop robust, distributed data pipelines for handling massive transaction volumes. Conduct deep exploratory data analysis to identify complex behavioral patterns and anomalies. - Lead Technical Groundwork & Best Practices: Own the end-to-end ML lifecycle, drive engineering excellence through code reviews, system design discussions, and establish data science methodologies. - Cross-Functional Prototyping & Expansion: Translate customer and business needs into ML solutions, design and build prototypes across various platform use cases, and optimize data quality and sample delivery. - Drive Agentic AI Innovation: Construct autonomous AI agents to monitor external channels, enforce data quality, and automate operational tasks like code documentation. - Advise & Communicate: Effectively communicate modeling choices, interpretations, and deliver demos and presentations to stakeholders. Qualifications Required: - Education: Advanced degree (Masters) in a quantitative STEM field or equivalent professional experience. - Experience: Minimum 5 years of hands-on experience applying data science to real-world challenges. - Advanced ML & Stats Fundamentals: Profound knowledge of statistical modeling, anomaly detection, evaluation metrics, and experimentation frameworks. - Programming Mastery: Proficiency in Python and advanced SQL. - Cloud & ML Frameworks: Strong expertise in traditional ML libraries and modern cloud-based MLOps pipelines. - Domain Expertise: Proven track record in fraud detection, cybersecurity, threat intelligence, or risk scoring. - Communication & Leadership: Excellent stakeholder-management skills and ability to communicate complex findings to non-technical audiences. Additional Company Details: PureSpectrum is dedicated to fostering a culture of innovation, connection, and providing a great experience for all team members. The company values flexibility, creativity, and open communication, offering a competitive compensation and benefits package, including health insurance, Provident Fund, and other perks supporting overall well-being. Team events, celebrations, and engagement activities strengthen the sense of community and belonging. PureSpectrum is committed to supporting team members both personally and professionally, empowering them to thrive inside and outside of work. The company is an equal opportunity employer, welcoming candidates from diverse backgrounds without discrimination based on various characteristics protected by law. Role Overview: As a Data Scientist at PureSpectrum, you will be part of the Data Science & AI team in Hyderabad. Your primary focus will be on fraud detection and threat mitigation within the market research ecosystem. You will play a crucial role in architecting real-time anomaly detection systems, contributing to feature engineering, and engaging in distributed computing to ensure data quality and reliability. Key Responsibilities: - Architect Real-Time Risk & Fraud Systems: Design, train, and deploy high-throughput machine learning models for detecting identity spoofing, survey fraud, and bot nets. Extend these capabilities to cybersecurity risk scoring, threat intelligence, and vendor risk assessments. - Process large scale transaction data: Develop robust, distributed data pipelines for handling massive transaction volumes. Conduct deep exploratory data analysis to identify complex behavioral patterns and anomalies. - Lead Technical Groundwork & Best Practices: Own the end-to-end ML lifecycle, drive engineering excellence through code reviews, system design discussions, and establish data science methodologies. - Cross-Functional Prototyping & Expansion: Translate customer and business needs into ML solutions, design and build prototypes across various platform use cases, and optimize data quality and sample delivery. - Drive Agentic AI Innovation: Construct autonomous AI agents to monitor external channels, enforce data quality, and automate operational tasks like code documentation. - Advise & Communicate: Effectively communicate modeling choices, interpretations, and deliver demos and presentations to stakeholders. Qualifications Required: - Education: Advanced degree (Masters) in a quantitative STEM field