Data Scientist Sr Associate

In this role you will serve as a hands-on individual contributor. You will help shape the technical direction of the team and build long-term firmwide capabilities to identify and prevent fraud leveraging cutting-edge techniques and modern cloud-based tools in an AWS environment.

Job Responsibilities:

• Develop train and deploy machine learning models for fraud prevention and risk management.
• Research and implement novel architectures including Graph Networks Agentic AI and Large Language Models.
• Build and test AI agents iterating designs to enhance functionality and user experience. Conduct rigorous testing to ensure reliability and effectiveness of AI solutions.
• Use tools like Databricks and PySpark to create data pipelines and dashboards that support AI-driven insights and decision-making.
• Monitor and optimize model performance in real-world environments adapting to evolving fraud patterns.
• Lead technical strategy and guide analytical direction within the team fostering a culture of innovation and continuous improvement.
• Mentor and support junior team members sharing best practices and technical expertise.
• Collaborate with cross-functional teamsincluding product engineering and data scienceto align modeling solutions with business objectives and firmwide priorities.
• Contribute to the development of scalable reusable machine learning solutions and best practices that strengthen the firms overall fraud prevention capabilities.

Required Qualifications Capabilities and Skills:

• Masters degree in Computer Science Mathematics Statistics Economics or a related quantitative field or equivalent work experience.
• Minimal 5-year of experience in developing and managing predictive riskmodels in financial institutions.
• Deep understanding of machine learning theory and algorithms with hands-on experience in both classical and deep learning methods.
• Proficient in Python SQL or PySpark with experience in deep learning frameworks such as PyTorch or TensorFlow and classical machine learning tools like XGBoost or Scikit-learn.
• Knowledge of graph analytics including GSQL will be an added bonus.
• Experience working with large datasets and building data pipelines using Databricks PySpark or similar technologies.
• Experience working in AWS cloud environments.
• Ability to build and test AI agents iterate designs and conduct rigorous testing for reliability and effectiveness.
• Experience mentoring or coaching junior team members.

Preferred/Additional Qualifications:

• Experience or strong interest in Graph Analytics and Agentic AI.
• Knowledge of GSQL.
• Deep technical understanding of the mathematics behind algorithms not just library usage.
• Product-first mindset with a focus on the role models play in the user experience and overall product responsibility.
• Versatility in handling both tabular and non-tabular data using classical machine learning (e.g. trees/forests) and modern deep learning techniques.
• Driven by impact and energized by the responsibility of having your models make decisions on live financial transactions.
• Demonstrated ability to build scalable reusable solutions that contribute to firmwide capabilities and long-term strategic goals.