Data Scientist II, Fraud & Risk

Socure

. Design, develop, and implement advanced deep learning models, including transformers, CNNs, and LSTMs, for fraud and risk challenges .

Posted 10/9/2026full-timeUnited StatesJuniorMid-Level💰 $140,000 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing advanced deep learning models, including transformers, CNNs, and LSTMs, while effectively collaborating in cross-functional teams. Proficient in Python and major ML libraries, with a strong foundation in machine learning algorithms and model deployment.

Highest-signal resume keywords

Deep Learning Model DevelopmentPython ProficiencyExperience with Transformers, CNNs, and LSTMsModel Deployment and MonitoringCollaboration in Cross-Functional Teams

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningTransformersConvolutional Neural NetworksLong Short-Term Memory NetworksModel Evaluation TechniquesData Pipeline DevelopmentFeature EngineeringMachine Learning AlgorithmsModel TrainingModel Optimization

Soft Skills

Problem-SolvingCommunication SkillsAbility to Work IndependentlyCollaboration

Tools & Technologies

PythonPyTorchTensorFlowScikit-learnLangChainLangGraphRay

Industry Keywords

Fraud PreventionRisk ModelingIdentity VerificationNatural Language ProcessingData Exploration

Tech Stack

Tools & technologies

PythonPyTorchRayScikit-LearnTensorflow

About the role

Key responsibilities & impact

Design, develop, and implement advanced deep learning models, including transformers, CNNs, and LSTMs, for fraud and risk challenges
Build and optimize models using tabular data, natural language, point clouds, and images
Own assigned tasks and execute technical and functional activities with minimal supervision
Participate in data exploration, feature engineering, model training, evaluation, and deployment
Collaborate across teams, sharing knowledge and learning from diverse perspectives
Make routine technical decisions and contribute to functional objectives
Stay current with AI and machine learning advancements and apply innovative approaches
Communicate results and insights to technical and non-technical audiences

Requirements

What you’ll need

Bachelor’s degree with substantial related experience, Master’s degree with relevant experience, or equivalent work background in Computer Science, Statistics, Mathematics, Engineering, or a related field
2–4 years of hands-on experience developing and deploying deep learning models, including transformers, CNNs, and LSTMs
Experience with diverse data modalities, including tabular data, text/language, point clouds, and images
Proficiency in Python and major ML libraries/frameworks such as PyTorch, TensorFlow, and scikit-learn
Foundational understanding of machine learning algorithms, model evaluation techniques, and data pipeline development
Experience with model deployment and monitoring in production environments
Experience with LLMs and Agentic AI frameworks/infrastructure such as LangChain, LangGraph, or Ray is a plus
Strong problem-solving skills and ability to work independently on straightforward tasks
Ability to collaborate in a diverse, cross-functional team environment
Excellent written and verbal communication skills
Experience in fraud prevention, risk modeling, or identity verification is a plus

Benefits

Comp & perks

Equity
Annual bonus
Competitive compensation
Benefits
Opportunities for professional growth and skill development
Inclusive, innovative culture
Accommodation support during application or hiring, including interview or onboarding support