Description

You will design and evolve graph data models for financial events, entities, and relationships.

Responsibilities

• Translate business questions into graph queries and features, including traversals, communities, and temporal patterns.
• Build data pipelines for ingestion, cleaning, labeling, and feature engineering, managing entity resolution and relationship extraction.
• Develop and validate statistical and ML models for risk scoring, anomaly detection, and forecasting.
• Create event-driven analytics using strong time semantics, focusing on event ordering and causality.
• Partner with engineering to productionize models, covering batch and near-real-time scoring, monitoring, and drift checks.

Required Skills

• 5+ years of experience in data science or a related field.
• Strong foundation in statistics and machine learning, including evaluation, leakage prevention, and experimentation.
• Hands-on experience with Graph DBs and graph concepts (node/edge types, temporal modeling).
• Proficiency in querying languages like Cypher (Neo4j) and/or Gremlin/SPARQL.
• Expertise in Python for data science, including pandas, numpy, and scikit-learn.
• Solid understanding of data engineering basics: SQL, ETL, data quality checks, and reproducibility.
• Ability to explain complex technical results to non-technical stakeholders.
• Experience with AWS or GCP cloud data stacks.
• Familiarity with ETL processes and data versioning.

Key Skills

Education

Any Gradute