Senior Data Scientist - Fraud Data Infrastructure & Automation
New
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience
- Required Skills
- PythonSQLPyTorchAirflowSparkTensorflowDatabricksscikit-learnLangChain
Requirements
- Master’s or PhD in a quantitative field (Computer Science, Statistics, Mathematics, Data Science) or equivalent experience.
- 5+ years of experience in data science, machine learning, or related roles in high-growth or fintech environments.
- Strong background in fraud prevention, risk modeling, or identity verification systems.
- Proven experience working with large-scale, messy, and real-world datasets to drive measurable impact.
- Proficiency in Python and SQL, with experience using ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Deep understanding of ML techniques, model evaluation metrics, and data pipeline development.
- Experience building distributed data workflows using tools such as Spark, Airflow, or Databricks.
- Familiarity with evaluating third-party data sources and designing data quality and lift experiments.
- Experience with LLMs and agentic AI frameworks (e.g., LangChain, LangGraph, Ray) is highly preferred.
- Strong communication skills with the ability to translate complex technical findings into business insights.
- Ability to lead technical initiatives, influence cross-functional teams, and work autonomously.
Responsibilities
- Design, build, and maintain scalable data pipelines and workflows supporting analytics, fraud detection, and model development.
- Develop and deploy machine learning models using diverse data types such as tabular, text, images, and other structured/unstructured formats.
- Build and integrate agentic AI and LLM-based systems to automate data exploration, anomaly detection, and investigative workflows.
- Ensure data quality, integrity, and reliability through monitoring systems, validation frameworks, and anomaly detection mechanisms.
- Lead full ML and analytics lifecycle ownership, from problem definition through deployment and post-launch monitoring.
- Evaluate third-party data vendors and external datasets, designing experiments to assess quality, lift, and business value.
- Partner with Product, Engineering, and Risk teams to define requirements and deliver insights that shape fraud and identity strategy.
- Conduct advanced research into new data sources, algorithms, and fraud detection techniques.
- Communicate findings and recommendations to both technical and executive stakeholders with clarity and impact.
- Mentor peers and contribute to a culture of experimentation, learning, and high analytical standards.
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