At Socure, we’re on a mission—to verify 100% of good identities in real time and eliminate identity fraud from the internet.
Using predictive analytics and advanced machine learning trained on billions of signals to power RiskOS™, Socure has created the most accurate identity verification and fraud prevention platform in the world. Trusted by thousands of leading organizations—from top banks and fintechs to government agencies—we solve real, high-impact problems at scale. Come join us!
As a Data Scientist at Socure, 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 our mission to eliminate identity fraud and advance digital trust across sectors.
Design, develop, and implement machine learning models and statistical algorithms to support the development of first party fraud (and other fraud modalities) 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 Socure’s platform.
Understand feedback/outcome and fraud contribution data and how it can improve Socure’s models and products across the board
Understand FCRA data and model design
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 Socure’s broader product strategies.
Experience working on fraud or fraud-adjacent data sets.
Master's degree or higher 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).
Create dashboard in AWS Quicksight and Databricks
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.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
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