As a Data Scientist in the organization, you will play a crucial role in building next-generation fraud and risk products that leverage cutting-edge machine learning algorithms and large-scale data processing. Working with cross-functional teams, you’ll analyze complex, high-volume datasets to develop and deploy models that drive business value and innovation. Your work will directly impact the mission to eliminate identity fraud and advance digital trust across sectors.

What You'll Do:

• Design, develop, and implement machine learning models and statistical algorithms to support the development of fraud detection and identity verification solutions, leveraging large-scale and diverse data sources.

• Analyze large datasets and uncover actionable insights, fraud patterns, and new opportunities for product and service enhancements across the organization’s platform.

• Collaborate with product, engineering, and cross-functional teams to translate business requirements into data-driven solutions that align with company goals.

• Develop and code data processing pipelines, automated workflows, and tools to cleanse, integrate, and evaluate data from multiple sources.

• Provide analytical support to the fraud and risk data science team; present findings and communicate data-driven insights with clear storytelling tailored to technical and non-technical audiences.

• Continuously test and apply the latest machine learning algorithms, libraries, and techniques to improve model performance and adaptability.

• Build, maintain, and monitor robust, scalable models deployed into production environments; participate actively in code reviews and peer discussions.

• Contribute to a collaborative, high-performance team environment; seek out and communicate trends, patterns, or anomalies that inform the organization’s broader product strategies.

What You Bring:

• Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent professional experience.

• Proficiency in Python (preferred) or R, with hands-on experience in machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.

• Demonstrated ability to analyze, clean, and model large-scale datasets using SQL and modern data tools (e.g., AWS, Databricks, Hadoop/Spark).

• Working knowledge of supervised and unsupervised learning, feature engineering, and model evaluation approaches.

• Experience translating business challenges into data science solutions and clearly communicating outcomes.