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# Data Scientist / Financial Events & Graph Analytics

- **Company:** NextGen Staffing
- **Location:** Berkeley Heights, NJ, United States
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Data Validation, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Neo4j, Node.Js, NumPy, SPARQL, SQL Databases, Management of Software Versions, Google Cloud, Feature Engineering, Indexer, Pandas, Scikit Learn, Production Code, Machine Learning Operations, Data Pipelines
- **Published:** June 12, 2026
- **Apply:** https://www.dice.com/job-detail/0226adf5-72aa-497b-9703-f560b4d30c08

## About the Role

* 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: accounts, payroll, employee details, KYE etc.
 * Understanding of financial events and workflows

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 (Google Cloud Platform/AWS), orchestration (Airflow/Prefect), and model serving.
 * Knowledge of governance/security patterns for sensitive financial data.

## Description

We are hiring a Data Scientist to model and analyze financial events and entity relationships using graph data. You will 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.

What you do

 * Design and evolve graph data models for financial events, entities, and relationships (accounts, payroll, employee details, KYE 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 produce models: batch + near-real-time scoring, monitoring, drift checks, and reproducible experiments.
 * Communicate findings clearly via notebooks, dashboards, and concise writeups.


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