Data Scientist II - Big Data R&D, Identity Graph & Deceased Monitoring

Why Socure?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

About the Role

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.

What You'll Do

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.

What You Bring

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).

Solid experience writing and optimizing SQL for large datasets; comfort working in data lake / warehouse environments.

Hands‑on experience with Spark or PySpark and common ML libraries (e.g., scikit‑learn, XGBoost, TensorFlow/PyTorch a plus).

Familiarity with UNIX environments and the AWS ecosystem (e.g., EMR, S3); Databricks experience is a plus.

Working knowledge of supervised/unsupervised ML and basic statistics (similarity measures, clustering, evaluation metrics).

Exposure to graph techniques or graph databases (Neo4j, AWS Neptune, GraphFrames) is a strong plus.

Bonus: experience with Elasticsearch or DynamoDB; workflow tools such as Airflow for automating data pipelines.