# Staff Data Scientist - Fraud & Risk

**Company:** [Socure](https://hotfix.jobs/companies/socure)
**Location:** Remote
**Role:** Data Science
**Salary:** $180k – $210k/yr
**Experience:** 8+ years
**Skills:** Python, SQL, PyTorch, TensorFlow, scikit-learn, Deep Learning, Transformers, Cnns, Rnns, Graph Learning, LLMs, LangChain
**Posted:** 2026-02-04

> Staff Data Scientist designs and deploys advanced deep learning models for fraud detection and risk management, leading ML lifecycle and mentoring peers. Requires 8+ years experience, Master's/PhD, and expertise in Python, PyTorch, and diverse data modalities.

## Job Description

## Responsibilities
- Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, 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.

## 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.
- 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 (real-time model inferencing is a plus).

## Nice-to-Haves
- Experience with **LLMs** and Agentic AI framework/infrastructure (e.g., **LangChain**, **LangGraph**, **Ray**).

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