Data Scientist

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 scheduleDuration: PermanentType: Full-timeRole summaryWe'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 doDesign 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 skillsStrong 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 modelingQuerying: Cypher (Neo4j) and/or Gremlin/SPARQLGraph algorithms: Page Rank, betweenness, connected components, community detection, similarityStrong 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-haveEntity 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