Socure Data Scientist II at Socure develops graph-based algorithms and data pipelines for identity verification and fraud detection. The role supports modelers and evaluates new data sources for identity and compliance products.
Responsibilities
The Big Data R&D team is responsible for building the core identity graph and entity-resolution capabilities that power Socure’s Deceased Monitoring and compliance products. In this role, you will help develop graph-based algorithms and data pipelines on massive PII datasets, support modelers with high-quality features, and evaluate new data sources that feed our identity and fraud products. You will work closely with senior data scientists and engineers while developing your skills in large-scale ML, distributed systems, and graph analytics.
Contribute to the design and implementation of machine learning, data mining, statistical, and graph-based algorithms to analyze very large datasets for identity verification and anomaly detection.
Analyze large datasets to help develop and refine entity-resolution and identity-matching algorithms that drive Socure’s Deceased Monitoring and compliance solutions.
Build and maintain components of data-processing pipelines (ETL, feature generation, normalization) using tools such as Spark/PySpark and AWS (e.g., EMR, S3).
Support senior data scientists with feature engineering, data exploration, error analysis, and A/B test setup for new models and signals.
Help evaluate new third‐party and internal data sources: profile data quality, design offline experiments, and summarize impact on coverage and model performance.
Implement and maintain SQL and Python/R code for data extraction, transformation, and validation; contribute to code reviews and basic testing.
Provide analytical support to compliance and regulatory product teams, including ad hoc investigations, simple dashboards, and data deep dives.
Communicate findings in a clear, structured way to peers and cross‐functional partners (Product, Engineering, Client Analysis), focusing on key insights and trade‐offs.
Work effectively in a fast‐paced, cross‐functional environment; demonstrate ownership of well-scoped tasks and follow through to completion.
Master’s degree with 2+ years of experience, or Ph.D. with 1+ years of experience in a data science or analytics role, or equivalent practical experience.
Proficiency in at least one general-purpose programming language used in data science (Python, or Scala).
Qualification
Required
Master’s degree with 2+ years of experience, or Ph.D. with 1+ years of experience in data science or analytics, or equivalent practical experience
Proficiency in at least one general-purpose programming language used in data science (Python or Scala)
