Socure is on a mission to verify 100% of good identities in real time and eliminate identity fraud from the internet. The Data Scientist 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 to solve real, high-impact problems at scale.
Requirements
- 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.
Responsibilities
- 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
- Develop and code data processing pipelines, automated workflows, and tools to cleanse, integrate, and evaluate data from multiple sources.
- 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.
Other
- 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.
- Collaborate with product, engineering, and cross-functional teams to translate business requirements into data-driven solutions that align with company goals.
- 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.
- Experience translating business challenges into data science solutions and clearly communicating outcomes.
