Senior Data Scientist - Fraud Data Infrastructure & Automation
New
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SocureFintech
Remote - USFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLPyTorchAirflowSparkTensorflowDatabricksscikit-learnLLMLangChain
Requirements
- 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.
- 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.
- 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.
Responsibilities
- 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.
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