Data Scientist II - Big Data R&D, Identity Graph & KYC

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 KYC 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.