Data Scientist II - Big Data R&D, Identity Graph & Deceased Monitoring
Data Scientist II developing graph-based algorithms, entity-resolution models, and large-scale data pipelines for identity and deceased-monitoring products. Requires advanced academic training or equivalent experience, strong Python/Scala and SQL skills, and hands-on Spark and AWS experience.
About the job
Responsibilities
- Design and implement machine-learning, data-mining, statistical, and graph-based algorithms for large-scale identity verification and anomaly detection.
- Analyze large datasets to develop and refine entity-resolution and identity-matching algorithms for deceased monitoring and compliance solutions.
- Build and maintain ETL, feature-generation, normalization, and other data-processing pipeline components using Spark/PySpark and AWS services such as EMR and S3.
- Support senior data scientists with feature engineering, data exploration, error analysis, and A/B test setup.
- Evaluate third-party and internal data sources, including data-quality profiling, offline experiments, and impact analysis on coverage and model performance.
- 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 through investigations, dashboards, and data deep dives.
- Communicate findings and trade-offs to Product, Engineering, Client Analysis, and other cross-functional partners.
Requirements
- Master’s degree with 2+ years of data science or analytics experience, Ph.D. with 1+ years of experience, or equivalent practical experience.
- Proficiency in Python or Scala.
- Experience writing and optimizing SQL for large datasets and working in data lake or data warehouse environments.
- Hands-on experience with Spark or PySpark and common machine-learning libraries.
- Familiarity with Unix environments and AWS.
- Working knowledge of supervised and unsupervised machine learning and basic statistics.
- Ability to break down loosely defined problems, ask clarifying questions, and iterate quickly.
Nice-to-haves
- TensorFlow or PyTorch.
- Databricks.
- Graph techniques or graph databases such as Neo4j, AWS Neptune, or GraphFrames.
- Elasticsearch or DynamoDB.
- Airflow for automating data pipelines.
Compensation
- Annual salary: $140,000–$170,000.
Skills
Python, Scala, SQL, Spark, Pyspark, AWS, Amazon Emr, Amazon S3, Machine Learning, scikit-learn, Xgboost, Graphframes, Neo4J, Airflow
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