Senior Data Scientist - Fraud, Risk & Production ML

Yugal Tech Academy; New York (NY); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading financial institution is seeking a Senior Data Scientist responsible for designing and deploying advanced machine learning models. You'll collaborate with teams to understand use cases such as fraud detection and credit risk assessment. The ideal candidate has expertise in Python and data science libraries, experience with large datasets, and knowledge of machine learning frameworks. This role involves working on diverse datasets and ensuring models are production-ready, while also creating insights through dashboards and visualizations.

Qualifications

• Strong expertise in Python and data science libraries like Pandas and NumPy.
• Experience with machine learning frameworks like TensorFlow or PyTorch preferred.
• Hands-on experience with large datasets and SQL for data manipulation.
• Solid understanding of statistics and probability.
• Familiarity with MLOps practices and cloud platforms.

Responsibilities

• Design and deploy machine learning models for financial operations.
• Collaborate with stakeholders to understand use cases.
• Work with large-scale structured and unstructured datasets.
• Build machine learning models using various techniques.
• Ensure model deployment and monitor performance.

Skills

Python

Data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib)

Machine learning frameworks (TensorFlow, PyTorch)

SQL

Problem-solving skills

Attention to detail

Tools

Spark

Hadoop

AWS

Azure

GCP

Job description

A leading financial institution is seeking a Senior Data Scientist responsible for designing and deploying advanced machine learning models. You'll collaborate with teams to understand use cases such as fraud detection and credit risk assessment. The ideal candidate has expertise in Python and data science libraries, experience with large datasets, and knowledge of machine learning frameworks. This role involves working on diverse datasets and ensuring models are production-ready, while also creating insights through dashboards and visualizations.