Technical Lead

About the Role

We’re hiring an ontologist 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.

Responsibilities

• 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.

Requirements

• Strong foundation in any of the following: Ontology, Ontology engineering, Knowledge modeling, OWL, RDF, SPARQL, SHACL, JSON-LD, Ontological inference, Consistency checking, Knowledge graphs, Property graphs, RDF graphs, Graph data modeling, Graph analytics, Ontology-driven KG design, Entity resolution, Semantic modeling.
• Expertise in tools such as Protégé, TopBraid Composer, OntoStudio, Neo4j, Stardog, GraphDB, Apache Jena, Fuseki, Blazegraph, Virtuoso.
• 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 data science (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.

Preferred Qualifications

• 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.
• 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.

Behavioral Skills

• Good communication skills.
• Flexibility to work rotational shifts and 5 days in-office (WFO).
• Team player with the ability to work in a changing environment.
• Strong problem-solving and analytical skills.
• Ability to work independently or within a team.
• Manage day-to-day challenges and communicate developmental risks with the technical team.

Pay

Salary range $80,500 to $157,500.