Data Scientist

About Us

NymCard's mission is to empower fintech and financial innovators to launch seamless payment programs using our cutting-edge infrastructure, rapidly and efficiently.

We are a team of industry experts and technology innovators who adopt a dynamic approach to addressing complex industry challenges. NymCard fosters an open and collaborative work environment, forming what we call the NymCardian Nation. We unlock possibilities for both our customers and ourselves by assembling top talent to deliver the highest quality work possible.

By adhering to our core values — Respect, Transparency, Collaboration, Quality, Speed, and Courage — NymCard aims to build a global team as diverse as the markets we serve. It is an exciting time to join NymCard, and as our business expands, so will your opportunities. To learn more about NymCard, visit our website and LinkedIn.

The Role

We are seeking a highly analytical and detail-oriented Data Scientist to join our dynamic team.

Responsibilities

• Develop, deploy, and maintain machine learning models for production use.
• Utilize deep learning frameworks like TensorFlow and Keras to build advanced models for various data challenges.
• Write reusable code libraries and packages for internal use.
• Collaborate with cross-functional teams (product, engineering, etc.) to implement data science solutions.
• Analyze and interpret financial data to guide business decisions, ensuring adherence to risk management principles.
• Perform various types of data analysis (descriptive, exploratory, inferential, causal, and predictive) to support business insights.
• Build and manage CI/CD pipelines for efficient model deployment and updates.
  
  

Qualifications

• 5+ years of experience as a data scientist or in a similar role.
• Proficiency in SQL, NoSQL and Python (including libraries like pandas, numpy, and scikit-learn).
• Experience with natural language processing (NLP) techniques.
• Experience with deep learning frameworks (TensorFlow, Pytorch, Huggingface etc.) and traditional machine learning algorithms.
• Familiarity with DevOps frameworks such as Git, Docker, and CI/CD processes.
• Demonstrated ability to create and deploy data science solutions in production environments.
• Solid understanding of financial data and risk management principles.
• Strong background in statistics, multivariable calculus, and linear algebra.
• Experience prototyping front-end solutions for showcasing machine learning models
• Experience with data visualization tools (e.g., Tableau, Power BI, or matplotlib).