Job Description

Role: Data Scientist — Financial Events & Graph Analytics (Graph DB / REA a Plus)

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

Duration : Permanent

Type : Full-time

Role summary

We’re hiring a Data Scientist to model and analyse financial events and entity relationships using graph data. You’ll 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.

What you’ll do

• Design and evolve graph data models for financial events, entities, and relationships (accounts, payments, invoices, trades, counter parties, ownership, etc.).
• Translate business questions into graph queries and features (traversals, communities, centrality, paths, temporal patterns).
• Build data pipelines for ingestion, cleaning, labelling, and feature engineering, including entity resolution and relationship extraction where needed.
• Develop and validate statistical/ML models (risk scoring, anomaly detection, fraud patterns, forecasting, classification).
• Create event-driven analytics using strong time semantics (event ordering, windows, causality assumptions, life-cycle states).
• Partner with engineering to productive models: batch + near-real-time scoring, monitoring, drift checks, and reproducible experiments.
• Communicate findings clearly via notebooks, dashboards, and concise write-ups.

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: Page Rank, 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

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