Senior Data Scientist – Big Data R&D, Identity Graph, KYC

Socure

. Own the design, development, and evaluation of machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on massive datasets.

Posted 4/24/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $140,000 - $170,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSNeo4jPySparkPythonPyTorchScalaSparkSQLTensorflow

About the role

Key responsibilities & impact

Own the design, development, and evaluation of machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on massive datasets.
Architect and optimize graph-based identity representations (identity graph structure, linkage rules, clustering) to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models.
Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets in AWS/Databricks environments.
Lead A/B tests and offline/online experimentation for new models, features, and data sources; define success metrics, design experiments, and ensure rigorous validation before rollout.
Evaluate new internal and external data sources: explore signal quality, design backtests, quantify incremental value, and provide clear recommendations on vendor selection and integration.
Partner closely with product managers and engineers to translate ambiguous business and regulatory requirements (e.g., KYC coverage, watchlist matching) into concrete modeling and data roadmaps.
Provide deep analytical support to Socure’s compliance and regulatory product suite, including investigative analyses, root‑cause analysis for anomalies, and clear narratives for internal and external stakeholders.
Contribute to model governance and documentation: clearly explain model logic, data dependencies, limitations, and monitoring plans to internal risk/compliance stakeholders.
Mentor junior data scientists and engineers on best practices in data exploration, feature engineering, experimentation, and code quality.
Communicate complex technical concepts and trade‑offs in a concise, structured way to both technical and non‑technical audiences (e.g., product reviews, customer meetings, internal briefings).

Requirements

What you’ll need

Master’s degree with 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience in applied ML / data science roles; background in Computer Science, Statistics, Mathematics, or related quantitative fields preferred.
Strong proficiency in Python (preferred) or Scala, including experience with ML libraries such as scikit‑learn, XGBoost, TensorFlow or PyTorch.
Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks) working on very large, messy datasets.
Deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, and experiment design (A/B testing, holdout strategies, stratification).
Experience developing production-quality data pipelines and automated workflows using Airflow or similar orchestration tools.
Practical familiarity with graph databases and/or graph frameworks (Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms for clustering, link prediction, and community detection is strongly preferred.
Solid SQL skills and experience working with large-scale analytical data stores.
Experience in at least one of: identity verification, fraud detection, credit risk, or adjacent high‑stakes domains is a plus.
Demonstrated ability to lead medium‑to‑large projects end‑to‑end, make sound trade‑off decisions under ambiguity, and influence cross‑functional stakeholders with data and clear reasoning.

Benefits

Comp & perks

Offers Equity
Offers Bonus

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningstatistical algorithmsgraph-based algorithmsentity-resolutionidentity trust scoringanomaly detectiondata normalizationfeature engineeringA/B testingmodel evaluation

Soft Skills

analytical supportmentoringcommunicationproject leadershipinfluencing stakeholdersproblem-solvingcollaborationclear reasoningvalidationdocumentation

Certifications

Master’s degreePh.D.