Senior, Data Scientist

About the role

The successful candidate will own fraud risks in various product segments and act as a strategic partner to product and business teams. They will design and implement graph-based fraud detection systems, including link analysis, entity resolution, and Graph Neural Network (GNN) models. Additionally, they will leverage Generative AI techniques to enhance fraud detection, automate investigation workflows, generate risk narratives, and improve model explainability.

Responsibilities

• Develop and implement machine learning models for fraud detection in areas such as E-Commerce & In-club payment fraud, Return abuse, Account takeover (ATO) etc.
• Design and implement graph-based fraud detection systems, including link analysis, entity resolution, and Graph Neural Network (GNN) models to detect coordinated fraud rings and network-level risk signals.
• Leverage Generative AI techniques (e.g., LLMs and Agentic workflows) to enhance fraud detection, automate investigation workflows, generate risk narratives, and improve model explainability.
• Partner with business and technical stakeholders to translate fraud business problems into data science solutions.
• Work on highly-scalable ML models and algorithms in big data mining, graph modeling & other domains.
• Work with engineering teams to implement model pipelines and deploy the service at scale.
• Swiftly respond to system issues and conduct root cause analysis.

Requirements

• Industry experience in building production machine learning systems at scale.
• 5-7 years of experience with languages used to manipulate data and draw insights from large data sets (e.g. Python, SQL, etc.).
• Experience working with large data sets and distributed computing tools (PySpark/GCP/BigQuery).
• Hands-on experience or strong familiarity with Graph Neural Networks (GNNs), graph embeddings, or large-scale graph processing frameworks.
• Experience exploring or applying Generative AI / LLM-based approaches for decision intelligence, feature generation, workflow automation, or explainability.
• Experience in fraud risk solutions is desirable.
• Experience in working with cross-functional product and engineering teams to understand requirements and incorporate them in the roadmap.

Qualifications

• Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 3 years' experience in an analytics related field.
• Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 1 years' experience in an analytics related field.
• 5 years' experience in an analytics or related field.

Skills

• Industry experience in building production machine learning systems at scale.
• Experience with large data sets and distributed computing tools (PySpark/GCP/BigQuery).
• Hands-on experience or strong familiarity with Graph Neural Networks (GNNs), graph embeddings, or large-scale graph processing frameworks.
• Experience exploring or applying Generative AI / LLM-based approaches for decision intelligence, feature generation, workflow automation, or explainability.
• Experience in fraud risk solutions is desirable.
• Experience in working with cross-functional product and engineering teams to understand requirements and incorporate them in the roadmap.

Benefits

• Competitive pay
• Performance-based bonus awards
• Health benefits
• Paid time off benefits
• Short-term and long-term disability
• Company discounts
• Military Leave Pay
• Adoption and surrogacy expense reimbursement
• Education benefit programs

Pay

$90,000.00 - $180,000.00 annually

Schedule

Flexible, hybrid work