Summary
Innovation is at the very core of BNY Mellon's DNA. Throughout history, we have always taken the lead to change and innovate. If you are a savvy technologist, have a learning mindset, and have the passion to build things, then this is the role for you! At BNY Mellon Enterprise Innovation, we transform ideas into extraordinary products and customer experiences.
We are looking for brilliant minds to join our diverse team of professionals to change, innovate and create the future of financial industry! With the right skillset, passion, commitment, the right mindset, and common sense, there is no limit to what we can accomplish together.
Key Qualifications
- Advanced degree in computer sciences, math, statistics or related discipline.
- Extensive experience in data modeling and data architecture skills.
- Excellent programming skills in Python, with proficient knowledge in coding standards and best practices.
- Hands-on experience with at least one popular machine learning framework such as TensorFlow, Keras, or Scikit-Learn, etc.
- Good understanding and application of MLOps on both cloud and on-premise infrastructure.
- Able to leverage on the tools and products to accelerate ML processes and data pipelines (H20, Cloudera, GCP, Azure, AWS, Airflow, JupyterLab, etc.)
- Knowledgeable and able to quickly learn different database querying languages (SQL, Cypher, etc).
- Strong analytical and problem solving skills.
- Resourceful, fast-learner, and independent requiring minimal supervision.
- Excellent communication and collaboration skills.
- Knowledge and experience in container technology is a plus (Docker, Kubernetes, etc).
Responsibilities
- Design, build and maintain machine learning systems and self-running AI software to automate predictive models.
- Build, automate and manage end-to-end data pipelines.
- Perform statistical analysis to resolve data set problems.
- Continuously, develop processes for automatic deployment of machine learning models upon improvements and train deep learning models at scale, and using industry best practices
- Collaborate with Data scientist to productionize trained models.
- Collaborate with SMEs and business stakeholders to gather and understand requirements/problem statements.
- Documentation of all software artifacts and functionalities implemented.
- Learn new and emerging technologies in the field of AI and Data Science.
- Work, collaborate and co-innovate with internal and external partners and promote an innovation culture.