data
Posted Feb 4Staff Data Scientist - Fraud & Risk
at Socure
United StatesHybrid
Responsibilities
- - 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.
- - 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.
Requirements
- As an advanced-level individual contributor, you will design, build, and optimize advanced DS/ML models that power our core fraud detection and risk management solutions.
- You will work hands-on with advanced deep learning models, driving delivery of impactful solutions for fraud detection, risk management, and identity verification.
- WHAT YOU'LL DO - Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges.
- - Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems.
- WHAT YOU BRING - 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/RNNs, and graph learning). -
- 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.
Contact
- Follow Us! YouTube https://www.youtube.com/c/Socure | LinkedIn https://www.linkedin.com/company/socure/ | X (Twitter) https://x.com/socureme | Facebook https://www.facebook.com/socure/
Additional details
- 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 We are seeking a skilled and motivated Staff Data Scientist to join our Fraud & Risk Data Science team.
- You will lead technical initiatives, mentor peers, and drive functional productivity and project success.
- This role requires deep technical expertise, strategic ownership, and a commitment to Socure’s leadership principles, including continuous learning, effective communication, and accountability.
- - Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges.