Senior Data Scientist - Big Data R&D, Identity Graph & KYC
New
Remote - USFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience
- Required Skills
- PythonSQLPyTorchAirflowSparkTensorflowScalaDatabricksscikit-learnPySpark
Requirements
- Master’s degree with 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience in applied ML / data science roles
- Background in Computer Science, Statistics, Mathematics, or related quantitative fields preferred
- Strong proficiency in Python (preferred) or Scala
- Experience with ML libraries such as scikit-learn, XGBoost, TensorFlow or PyTorch
- Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks)
- Experience working on very large, messy datasets
- Deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, and experiment design (A/B testing, holdout strategies, stratification)
- Experience developing production-quality data pipelines and automated workflows using Airflow or similar orchestration tools
- Practical familiarity with graph databases and/or graph frameworks (Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms for clustering, link prediction, and community detection is strongly preferred
- Solid SQL skills and experience working with large-scale analytical data stores
- Experience in at least one of: identity verification, fraud detection, credit risk, or adjacent high-stakes domains is a plus
- Demonstrated ability to lead medium-to-large projects end-to-end, make sound trade-off decisions under ambiguity, and influence cross-functional stakeholders with data and clear reasoning
Responsibilities
- Own the design, development, and evaluation of machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on massive datasets.
- Architect and optimize graph-based identity representations (identity graph structure, linkage rules, clustering) to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models.
- Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets in AWS/Databricks environments.
- Lead A/B tests and offline/online experimentation for new models, features, and data sources; define success metrics, design experiments, and ensure rigorous validation before rollout.
- Evaluate new internal and external data sources: explore signal quality, design backtests, quantify incremental value, and provide clear recommendations on vendor selection and integration.
- Partner closely with product managers and engineers to translate ambiguous business and regulatory requirements (e.g., KYC coverage, watchlist matching) into concrete modeling and data roadmaps.
- Provide deep analytical support to Socure’s compliance and regulatory product suite, including investigative analyses, root-cause analysis for anomalies, and clear narratives for internal and external stakeholders.
- Contribute to model governance and documentation: clearly explain model logic, data dependencies, limitations, and monitoring plans to internal risk/compliance stakeholders.
- Mentor junior data scientists and engineers on best practices in data exploration, feature engineering, experimentation, and code quality.
- Communicate complex technical concepts and trade-offs in a concise, structured way to both technical and non-technical audiences (e.g., product reviews, customer meetings, internal briefings).
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