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About
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).
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).
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.
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.
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Languages
- English
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