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Data Scientist

Berkeley Heights, New Jersey, United StatesHybridFull TimePosted 2 months agoVisa sponsorship available

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Role summary

We are seeking a Data Scientist with expertise in financial events and graph analytics to join our team. This role involves designing graph data models, building analytical pipelines, and developing ML models for insights like risk signals and anomaly detection. You will work with engineers and stakeholders to translate business needs into graph queries and productionize models. Familiarity with REA accounting/event modeling is a plus. The position requires a hybrid work model, with 3 days in Berkeley Heights, NJ, and 2 days in Princeton, NJ.

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

Full time

Location: Berkeley Heights, NJ (3 Days) 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.
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