Data Scientist II – Big Data R&D, Identity Graph, KYC
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Job Description
Data Scientist II developing graph-based algorithms and data pipelines for identity verification. Building core capabilities for Socure’s KYC and fraud products while collaborating with senior data scientists and engineers.
Responsibilities:
- 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.
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).
- 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.
- Ability to break down loosely defined problems, ask good clarifying questions, and iterate quickly with feedback.
Benefits:
- Offers Equity
- Offers Bonus
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