Senior Data Scientist - International eKYC, Identity Graph

United StatesHybrid

Responsibilities

- Architect reusable matching and linking frameworks that work across multiple ID schemes (e.g., national ID numbers, passports, voter IDs, mobile accounts, bank accounts) and local name/address conventions.
- Develop probabilistic and rule‑augmented models that handle noisy, sparse, or partially labeled international data while maintaining explainability and regulatory defensibility.
- Design and implement robust data quality and monitoring frameworks for international identity data (coverage, stability, drift, regional bias, label quality) and integrate them into modeling and production monitoring workflows.
- Build scalable approaches for handling linguistic and cultural variation (e.g., transliteration, multi‑script names, address normalization, local naming patterns) in the identity graph and matching pipelines.
Evaluation, Experimentation, and Model Governance - Own experimentation strategy for major international eKYC initiatives: - Design offline evaluations and online A/B tests that reflect local ground truth constraints and data sparsity.
Data Source Strategy & Vendor Evaluation (International) - Lead the evaluation and integration of international data vendors (e.g., bureaus, telcos, public records, alternative data): - Design benchmarking methodologies for signal quality, incremental value, stability, and fairness by country/segment.

You will design and deploy ML and graph-based systems tailored to diverse international markets, regulations, and data ecosystems—covering government IDs, telco and credit bureaus, mobile-first data, and non‑traditional signals.
You will closely partner with Product, Engineering, Compliance, and GTM teams to launch and scale eKYC solutions across multiple countries and regions.
WHAT YOU'LL DO International eKYC Modeling & Entity Resolution - Lead the design, development, and deployment of ML and graph-based algorithms for international entity resolution, identity trust scoring, and anomaly detection across heterogeneous, country‑specific datasets.
- Analyze lift, stability, and fairness trade‑offs and drive go/no‑go decisions with Product and Engineering.
Experience - Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience. - 6+ years of hands-on applied ML / data science
Technical Skills - Expert‑level proficiency in Python and SQL, with extensive
experience in distributed data processing (Spark/PySpark, Databricks or similar) on very large datasets. - Deep
- Proven expertise with graph technologies (e.g., Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms (entity resolution, link prediction, community detection, label propagation) at scale.

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WHY SOCURE? Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts.
The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility.
People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision.
If you want predictability or narrow scope, this won’t be your place.
If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
ABOUT THE ROLE The Big Data R&D team builds the core entity‑resolution and graph‑based intelligence that underpins Socure’s Verify and KYC products.
You will own complex, cross‑product initiatives such as international identity graph evolution, probabilistic matching for non‑US identities, and scalable evaluation frameworks that account for regional regulatory and fairness constraints.
Global Identity Graph & Data Quality - Define and evolve the international extension of Socure’s identity graph: schema design, linkage strategies, quality tiers, and confidence scoring that can be leveraged by multiple products (Verify, KYC, watchlists, fraud).
- Define success metrics that balance approval rates, fraud capture, and regulatory/operational constraints per market.