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Job Summary
Synechron is seeking an experienced Fraud Analytics Strategist to lead the development and implementation of fraud detection and prevention strategies within the organization. This position involves analyzing large datasets, developing sophisticated fraud rules, and supporting the deployment of these strategies in fraud management platforms. The successful candidate will leverage extensive expertise in fraud risk systems, data analysis, and visualization to reduce fraud losses while maintaining an optimal customer experience and operational balance. This role plays a critical part in safeguarding the organization’s assets, reputation, and compliance.
Software Requirements
Required:
Proficiency with fraud risk management tools such as Falcon FM, SAS FM, FICO Blaze, MasterCard Brighterion, MasterCard DI, and VRM.
Strong expertise in SQL for data extraction, query optimization, and data manipulation.
Skilled in Python and R for data analysis, model development, and automation.
Experience with Excel VBA for advanced data processing and reporting.
Familiarity with data visualization tools and techniques for presenting insights to stakeholders.
Knowledge of data security practices and regulatory compliance standards (GDPR, PCI DSS, etc.).
Preferred:
Experience deploying fraud models in production systems.
Exposure to big data environments such as Spark or Hadoop.
Familiarity with APIs and integrating fraud rules into fraud management platforms.
Knowledge of cloud platforms like AWS (S3, EC2, Lambda) or Azure for data processing and deployment.
Overall Responsibilities
Develop and refine fraud detection strategies, rules, and models based on data insights.
Analyze large volumes of transactional and behavioral data to identify fraud patterns and anomalies.
Support testing, deployment, and tuning of fraud detection models on fraud management platforms.
Conduct root cause analysis of fraud cases, provide insights, and recommend process improvements.
Collaborate with cross-functional teams, including data science, IT, and business units, to ensure timely and effective fraud mitigation.
Ensure data quality, security, and compliance in all analytics processes.
Monitor, evaluate, and report on fraud detection performance metrics; suggest enhancements.
Performance outcomes:
Achieve measurable reductions in fraud losses, optimize fraud detection accuracy, and enhance operational efficiency with minimal false positives.
Technical Skills (By Category)
Programming Languages:
Essential: SQL, Python, R
Preferred: SAS, Java
Data & Data Management:
Experience with large databases (SQL, NoSQL), data modeling, and data validation techniques.
Fraud & Risk Systems:
In-depth knowledge of fraud detection platforms such as Falcon FM, SAS FM, FICO Blaze, BrightKey, or VRM.
Analytics & Model Development:
Expertise in statistical analysis, anomaly detection, machine learning modeling, and pattern recognition.
Cloud Technologies:
Preferred: AWS (S3, Lambda, EC2), Azure, or other cloud services for data security and deployment.
Tools & Libraries:
Data analysis and visualization: Pandas, NumPy, Matplotlib, Seaborn, Tableau (preferred).
Automation: Python scripting, API integration.
Security & Compliance:
Understanding of data encryption, role-based access, and compliance standards.
Experience Requirements
Minimum of 7+ years supporting fraud risk management, analytics, or related functions within financial services or enterprise environments.
Proven experience developing, validating, and deploying fraud detection rules and models.
Demonstrated success in analyzing large datasets, identifying fraud patterns, and supporting model deployment.
Experience working with cross-functional teams, including data scientists, IT, and business units.
Day-to-Day Activities
Analyze transactional data to identify fraud trends and suspicious activities.
Develop, test, and implement predictive rules and models for fraud detection.
Support the deployment of fraud detection systems and continuously optimize for performance.
Conduct root cause analysis on fraud cases to improve detection strategies.
Collaborate with data and software teams to upgrade fraud management platforms.
Monitor system performance, review alerts, and adjust rules as needed.
Generate reports on fraud trends, detection accuracy, and system performance for senior management.
Qualifications
Bachelor’s or Master’s degree in Technology, Statistics, Mathematics, Data Science, or equivalent.
7+ years of experience in fraud analytics, risk management, or related analytics functions.
Proven experience working with fraud detection tools, complex queries, and big data.
Certifications in fraud prevention, data analysis, or related fields are a plus.
Strong presentation, communication, and stakeholder management skills.
Professional Competencies
Critical thinking for analyzing fraud patterns and assessing risk.
Leadership and mentoring skills to guide junior analysts and teams.
Effective communication skills to translate technical findings into actionable insights.
Adaptability to new threats, technologies, and regulatory standards.
Ownership and accountability for fraud mitigation initiatives.
Time management skills for prioritizing investigations and reporting deadlines.
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
Candidate Application Notice
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