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Über
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)
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:
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.
Sprachkenntnisse
- English
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