Director, Data Scientist

BNY MellonNew York, NY
$160,000 - $260,000Onsite

About The Position

At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide. Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary. We’re seeking a future team member for the role of Director, Data Science to join our Engineering team. This role location is New York City. Own data exploration, feature engineering, and model development and evaluation to transform complex data into actionable insights and support AI model provisioning. Partner closely with engineering teams to design, deploy, and monitor models responsibly in alignment with business objectives.

Requirements

  • Strong foundation in statistics and machine learning.
  • Proficiency in Python, including experience with pandas, scikit-learn, and XGBoost.
  • Experience with experimental design and A/B testing methodologies.
  • Ability to connect technical model outputs to business needs and domain context.
  • Familiarity with practical LLM evaluation techniques.
  • Demonstrated ability to work cross-functionally with technical and business stakeholders.

Responsibilities

  • Lead data exploration and feature engineering activities to support model development.
  • Develop, evaluate, and refine statistical and machine learning models.
  • Translate complex data into actionable business insights.
  • Design experiments and A/B tests to assess model and business performance.
  • Define and track business-aligned success metrics.
  • Apply model explainability techniques to support transparent and responsible AI usage.
  • Collaborate closely with engineering partners to deploy, monitor, and maintain models in production.

Benefits

  • Generous paid leaves
  • Paid volunteer time
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