Title: Data Scientist — Financial Events & Graph Analytics (Graph DB / REA a Plus)
Location: Berkeley Heights, NJ and Princeton, NJ(hybrid)
Full Time only
Role summary
We’re hiring a Data Scientist to model and analyze 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, counterparties, ownership, etc.).
· Translate business questions into graph queries and features (traversals, communities, centrality, paths, temporal patterns).
· Build data pipelines for ingestion, cleaning, labeling, 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, lifecycle states).
· Partner with engineering to productionize models: batch + near-real-time scoring, monitoring, drift checks, and reproducible experiments.
· Communicate findings clearly via notebooks, dashboards, and concise writeups.
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:
o Schema/design: node/edge types, properties, constraints, indexing, cardinality, temporal modeling
o Querying: Cypher (Neo4j) and/or Gremlin/SPARQL
o 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.
