Design, develop, and implement advanced deep learning models, including transformers, CNNs, and LSTMs, to address complex fraud and risk challenges.
Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images.
Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments.
Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges.
Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning.
Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions.
Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection.
Present findings and recommendations to technical and executive stakeholders with clarity and influence.
Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems.
Model Socure’s embedded leadership competencies: continuous learning, effective communication, accountability, team development, decision making, and managing change.

Requirements

Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field; or equivalent professional experience.
8+ years of experience in data science, machine learning, or related fields, ideally in a high-growth tech or fintech environment.
Experience in fraud prevention, risk modeling, or identity verification.
Years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs, and LSTMs).
Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images.
Strong proficiency in Python, SQL, and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development.
Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus).
Experience with LLMs and Agentic AI framework/infrastructure (e.g., LangChain/LangGraph/Ray) is a plus.
Demonstrated ability to proactively deliver complex outcomes, mentor others, and influence cross-functional decisions.
Excellent communication skills with the ability to translate complex data problems into actionable business insights for both technical and non-technical audiences.
Commitment to continuous learning, professional integrity, and high standards of business ethics.

Benefits

Offers Equity
Offers Bonus

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

deep learningtransformersCNNsLSTMsPythonSQLPyTorchTensorFlowscikit-learnmachine learning algorithms

Soft Skills

effective communicationmentoringaccountabilityteam developmentdecision makingmanaging changeproactive deliverycollaborationinfluencecontinuous learning

Certifications

Master’s in Computer SciencePhD in Computer ScienceMaster’s in StatisticsMaster’s in Applied MathematicsMaster’s in Data Science