Reference #: 55497 Note: This is a pipeline requisition and does not represent any one particular job opening. By applying to this pipeline requisition, your interest will be reviewed for multiple potential Third-Party Governance openings based upon your background and disclosed work preference. Bring your ideas. Make history. BNY Mellon offers an exciting array of future-forward careers at the intersection of business, finance, and technology. We are one of the world's top asset management and banking firms that manages trillions of dollars in assets, custody and/or administration. Known as the "bank of banks" - 97% of the world's top banks work with us as we lead and serve our customers into the new era of digital. With over 238 years of rich history and industry firsts, BNY Mellon has been built upon our proven ability to evolve, lead, and drive new ideas at every turn. Today, we're approximately 50,000 employees across 35 countries with a culture that empowers you to grow, take risks, experiment and be yourself. This is what #LifeAtBNYMellon is all about. We're seeking a future team member for the role of Data Scientist to join our Clearing Markets Treasury Engineering team. This role is located in Pittsburgh, PA and Lake Mary, FL, and will work a Hybrid schedule (3 days per week in-office required).  Candidate must reside within a commutable distance to this work location, as we are unable to accommodate 100% virtual work arrangements. In this role, you'll make an impact in the following ways: Apply advanced machine learning and deep learning techniques to solve complex problems and generate insights from data. Develop, test, and deploy scalable and robust AI models and pipelines using various frameworks and tools. Collaborate with cross-functional teams of engineers, product managers, and domain experts to understand the business needs and deliver high-quality solutions. Communicate the results and implications of the AI projects to both technical and non-technical stakeholders. Stay updated with the latest research and trends in AI and related fields. Accountable for developing and guiding more junior members and communicating findings, approaches, and insights from data products. Provides technical and organizational leadership across multiple areas the organizational and functional effectiveness of data science products. Additional responsibilities: Conducts studies to provide additional facts needed to make informed decisions about organizational and functional effectiveness with data decisioning. Develops consultative partnerships with internal teams to understand their strategic objectives, key performance indicators and reporting requirements. Monitors industry and marketplace technology practices and benchmarks related to banking industry and supporting the data strategy. Grow and develop skills across the 3 domain specialties: model science, feature science and Insight science capabilities. Prior working knowledge of: Computer Programming, Math & Analytics Methodology, machine learning algorithms/frameworks, big data technologies, Distributed computing, and communications of complex results. Establishes relationships to obtain data and subject knowledge needed to support advanced analytics. Extracts business insights to guide direction of learning activities and drive organizational efficiencies, effectiveness, and outcomes. Stays abreast of technology innovations and trends and identifies areas of improvement across BNY Mellon Collaborates with and supports leaders and their teams from multiple areas of expertise to ensure strategies and data science products are aligned and support business and operating results. To be successful in this role, we're seeking the following: Advanced degree in STEM engineering degrees, or equivalent work experience with experience preferred in related fields. 4-9 years of related experience required; experience in the securities or financial services in ustry is a plus. At least 2 years of experience in developing and deploying AI solutions using Python, TensorFlow, PyTorch, or similar frameworks. Strong knowledge of machine learning and deep learning concepts, algorithms, and best practices. Proficient in data analysis, manipulation, and visualization using tools such as Pandas, NumPy, Scikit-learn, Matplotlib, or similar. Experience in working with various data sources and formats, such as structured, unstructured, text, image, video, audio, etc. Excellent communication, presentation, and teamwork skills. Ability to work independently and creatively in a fast-paced environment. Passion for learning new technologies and solving challenging problems. Knowledge of development processes/lifecycle management for large language models Add to qualifications: Knowledge and understanding for leveraging: Predictive Analytics, Automation, Anomaly Detection/Risk Management, and Generative AI (GenAI) At BNY Mellon, our inclusive culture speaks for itself. Here's a few of our awards: Fortune World's Most Admired Companies & Top 20 for Diversity and Inclusion Bloomberg's Gender Equality Index (GEI) Best Places to Work for Disability Inclusion, Disability: IN - 100% score 100 Best Workplaces for Innovators, Fast Company Human Rights Campaign Foundation, 100% score Corporate Equality Index CDP's Climate Change 'A List' Our Benefits: BNY Mellon offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay-for-performance philosophy. We provide access to flexible global resources and tools for your life's journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves that can support you and your family through moments that matter. BNY Mellon is an Equal Employment Opportunity/Affirmative Action Employer - Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.

BNY Mellon is an Equal Employment Opportunity/Affirmative Action Employer. Minorities/Females/Individuals With Disabilities/Protected Veterans.