Data Scientist Sr. Data Scientist
Share
Job Location:
Berkeley Heights, NJ - USA
Monthly Salary:
Not Disclosed
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
| Skills | Location | |
| Data Scientist | Berkeley Heights NJ / Princeton NJ | |
| Data Scientist | Berkeley Heights NJ / Princeton NJ |
Rate: upto $130k
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
Were hiring a Data Scientist to model and analyze financial events and entity relationships using graph data. Youll 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 youll 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.
