Senior Data Scientist – Fraud Data Infrastructure, Automation

Socure

. Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems).

Posted 4/29/2026full-timeRemote • District of Columbia, New York • 🇺🇸 United StatesSenior💰 $170,000 - $200,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowDistributed SystemsPythonPyTorchRayScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems).
Leverage and build agentic AI and LLM-powered systems to automate data exploration, anomaly detection, vendor evaluation, and investigative workflows, increasing the speed and depth of insight generation.
Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images, in support of fraud detection and identity verification use cases.
Own data quality and integrity for critical datasets, implementing monitoring, validation checks, and anomaly detection to ensure reliable input to models and downstream decision systems.
Take ownership of project outcomes from scoping through delivery, managing data quality, technical trade-offs, and timelines; proactively escalate risks and work cross-functionally to resolve challenges.
Evaluate and integrate third-party data vendors and external datasets, including designing experiments to assess data quality, coverage, lift, and long-term value for Socure’s models and products.
Collaborate closely with Product, Engineering, and Risk teams to define data requirements, shape roadmap priorities, and deliver insights that guide strategic decisions for fraud and identity products.
Conduct in-depth research to explore new data sources and develop novel algorithms and features that advance the state of the art in fraud detection, identity resolution, and risk scoring.
Lead the end-to-end ML/analytics lifecycle for assigned projects: problem definition, data exploration, feature engineering, modeling, evaluation, deployment handoff, and post-deployment monitoring where applicable.
Present findings, trade-offs, and recommendations to technical and executive stakeholders with clarity and influence, adapting communication for audiences ranging from engineers to non-technical business leaders.
Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning aligned to Socure’s leadership competencies.
Stay current with advancements in AI, machine learning, and data infrastructure (including LLMs and agentic frameworks), and apply innovative techniques to real-world fraud and identity problems.
Model Socure’s embedded leadership competencies in day-to-day work: continuous learning, effective communication, accountability, team development, decision making, and managing change.

Requirements

What you’ll need

Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related quantitative field; or equivalent professional experience.
5+ years of experience in data science, machine learning, or closely related roles, ideally in a high-growth tech or fintech environment.
Experience in fraud prevention, risk modeling, or identity verification, including working with noisy, adversarial, or high-risk data environments.
Proven experience working with large, messy, real-world datasets to generate insights and drive measurable business impact (not limited to pure model development).
Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images, and selecting appropriate modeling approaches for each.
Strong proficiency in Python and SQL, with hands-on experience using major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn) for model development and evaluation.
Deep understanding of machine learning algorithms, model evaluation techniques (e.g., AUC, lift, calibration, stability), and data pipeline development for both batch and near-real-time use cases.
Experience building and maintaining data pipelines and workflows in distributed or large-scale environments (e.g., Spark, Airflow, Databricks, or similar technologies).
Demonstrated ability to evaluate and work with third-party data vendors or external datasets, including designing tests for data quality, coverage, stability, and incremental lift over existing signals.
Experience with LLMs and agentic AI frameworks/infrastructure (e.g., LangChain, LangGraph, Ray) is strongly preferred; ability to design or extend agentic workflows for analytics and data quality use cases is a plus.
Demonstrated ability to proactively deliver complex outcomes, lead technical workstreams, mentor others, and influence cross-functional decisions without formal authority.
Excellent written and verbal communication skills, with the ability to translate complex data problems and model behavior into actionable business insights for both technical and non-technical audiences.
Commitment to continuous learning, professional integrity, and high standards of business ethics, consistent with Socure’s leadership expectations.

Benefits

Comp & perks

Offers Equity
Offers Bonus

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data pipelinesmachine learningmodel developmentdata explorationfeature engineeringanomaly detectiondata qualitymodel evaluationPythonSQL

Soft Skills

effective communicationmentoringproblem definitioncross-functional collaborationaccountabilitydecision makingcontinuous learninginfluenceproject managementadaptability

Certifications

Master’s in Computer SciencePhD in StatisticsPhD in Applied MathematicsPhD in Data Science