Data Science ( Data Scientist )

Title: Data Scientist - Financial Events & Graph Analytics (Graph DB / REA a Plus)

Location: Berkeley Heights NJ (53Days) and Princeton NJ(2 Days) (based on client schedule)

Full time role

Role summary

Need someone with Ontology exp.

Were hiring a Data Scientist to model and analyze financial events and entity relationships using graph data. Youll work with engineers and stakeholders to design graph schemas build analytical pipelines and deliver insights/products such as risk signals anomaly detection entity resolution and event-driven intelligence. Familiarity with REA (Resources Events Agents) accounting/event modeling is a plus.

Must-have skills

• Strong foundation in statistics machine learning (evaluation leakage prevention bias checks calibration experimentation).
• Hands-on experience with Graph DBs and graph concepts:
  • Schema/design: node/edge types properties constraints indexing cardinality temporal modeling
  • Querying: Cypher (Neo4j) and/or Gremlin/SPARQL
  • Graph algorithms: PageRank betweenness connected components community detection similarity
• Strong Python for DS (pandas numpy scikit-learn; comfort writing production-ready code).
• Solid data engineering basics: SQL ETL data quality checks versioning reproducibility.
• Ability to explain technical results to non-technical stakeholders.

Domain experience (preferred)

• Financial data and event modeling: payments reconciliation ledgers trades positions KYC/AML signals counterparty networks.
• Understanding of financial events and workflows (authorization capture settlement invoice payment reconciliation trade lifecycle etc.).
• REA (Resources Events Agents) modeling and/or accounting event-sourcing concepts is a strong plus

Nice-to-have

• Entity resolution / record linkage; graph-based identity resolution.
• NLP for event extraction from unstructured text (contracts filings invoices).
• Experience with cloud data stacks (GCP/AWS) orchestration (Airflow/Prefect) and model serving.
• Knowledge of governance/security patterns for sensitive financial data.