Socure
Data Scientist II - Big Data R&D, Identity Graph & Deceased Monitoring
Hybrid - San Francisco, CA · Mid
No sponsorshipDetected 50 days ago
PythonScalaDistributed SystemsAlgorithmsCode ReviewSQLElasticsearchDynamoDBDatabricksAWSLinuxMachine LearningTensorFlowPyTorchSparkAirflowData EngineeringData ScienceStatisticsA/B TestingEHR/EMR
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odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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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.
Responsibilities
- 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.
- Implement and maintain SQL and Python/R code for data extraction, transformation, and validation
- 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.
Requirements
- 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).
- comfort working in data lake / warehouse environments.
- Working knowledge of supervised/unsupervised ML and basic statistics (similarity measures, clustering, evaluation metrics).
- Ability to break down loosely defined problems, ask good clarifying questions, and iterate quickly with feedback.
- and that you must be located within 45 miles of a talent hub to be considered.
- Solid experience writing and optimizing SQL for large datasets
- Bonus: experience with Elasticsearch or DynamoDB
- Please note that sponsorship is not available at this time
Nice to have
- 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.
- Exposure to graph techniques or graph databases (Neo4j, AWS Neptune, GraphFrames) is a strong plus.
- workflow tools such as Airflow for automating data pipelines.
Benefits
- 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.
- Help evaluate new third‑party and internal data sources: profile data quality, design offline experiments, and summarize impact on coverage and model performance.
Visa & Work Authorization
- Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.
This listing is sourced directly from Socure's careers page and normalized into a canonical job model.