Staff Data Scientist - Fraud & Risk

Socure Socure · Vertical AI · United States · Remote · Data Science & AI

Staff Data Scientist focused on designing, building, and optimizing advanced deep learning models for fraud detection and risk management in a fintech environment. The role involves leading technical initiatives, mentoring peers, and driving the end-to-end ML lifecycle from data exploration to production deployment and monitoring. Experience with diverse data modalities and LLMs/Agentic AI is a plus.

What you'd actually do

  1. Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges.
  2. Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images.
  3. Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments.
  4. Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges.
  5. Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning.

Skills

Required

  • Python
  • SQL
  • PyTorch
  • TensorFlow
  • scikit-learn
  • deep learning models
  • transformers
  • CNNs/RNNs
  • graph learning algorithms
  • tabular data
  • natural language
  • point clouds
  • images
  • machine learning algorithms
  • model evaluation techniques
  • data pipeline development
  • model deployment
  • model monitoring
  • fraud prevention
  • risk modeling
  • identity verification

Nice to have

  • LLMs
  • Agentic AI framework/infrastructure
  • LangChain
  • LangGraph
  • Ray
  • real-time model inferencing

What the JD emphasized

  • advanced deep learning models
  • transformers, CNNs/RNNs, and graph learning algorithms
  • fraud detection
  • risk management
  • identity verification
  • end-to-end machine learning lifecycle
  • model deployment and monitoring in production environments
  • real-time model inferencing

Other signals

  • design, build, and optimize advanced DS/ML models
  • lead technical initiatives
  • drive functional productivity and project success
  • hands-on with advanced deep learning models
  • driving delivery of impactful solutions for fraud detection, risk management, and identity verification