Vice President; Quantitative Finance Analyst

Key Skills

AWS, Azure, Bash scripting, C++, CUDA/GPU programming, Deep Learning, Design, Docker, Finance, Google Cloud Platform (GCP), HDFS, KPI, Kubernetes-K8s, Machine learning techniques, Oracle, Python Programming, Terraform

Job Description

• Apply quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements.
• Maintain and enhance capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks.
• Understand and execute activities that form the end‐to‐end model development and use life cycle.
• Document and communicate quantitative methods as part of ongoing engagement with key stakeholders, including the lines of business, risk managers, model validation, technology.
• Develop, deploy, and implement various machine learning and deep learning models using computer vision, computer memory and graphical processing unit (GPU) computing technologies.
• Query or collect data from different data sources including Hadoop (HDFS), Oracle, Google Cloud platform, Azure, AWS.
• Use unstructured data from KPIs and programming languages and domains including C++, OpenGL, CUDA, MPI, Bash, Terraform, Docker, Kubernetes, Python.
• Create Machine Learning Proposals, Business, Design documents of all the deliverables and KPI’s.
• Liaise with internal and external stakeholders and deep dive discussions with other technical leaders for solution alignment with strategy and standards and act as a subject matter expert (SME) for machine learning/ deep learning.

REQUIRED SKILLS & EXPERIENCE :

• Master's degree or equivalent in Data Science, Finance, Mathematics, or related; and
• 3 years of experience in the job offered or a related quantitative occupation.
• Must include 3 years of experience in each of the following:
• Developing, deploying, and implementing various machine learning and deep learning models using computer vision, computer memory and graphical processing unit (GPU) computing technologies;
• Querying or collecting data from different data sources including Hadoop (HDFS), Oracle, Google Cloud platform, Azure, AWS;
• Using unstructured data from KPIs and programming languages and domains including C++, OpenGL, CUDA, MPI, Bash, Terraform, Docker, Kubernetes, Python;
• Creating Machine Learning Proposals, Business, Design documents of all the deliverables and KPI’s; and,
• Liaising with internal and external stakeholders and deep dive discussions with other technical leaders for solution alignment with strategy and standards and act as a subject matter expert (SME) for machine learning/ deep learning.