Data Scientist

Federal Reserve System•New York, NY

2d•$170,000 - $230,000•Onsite

About The Position

The Data & Analytics Office (DAO) at the Federal Reserve Bank of New York drives the enterprise data and analytics strategy for the Bank and owns data policy and governance standards. The DAO is responsible for establishing a data foundation for the Bank, building capabilities while setting standards and measurements for data quality, compliance, security, and retention of our data assets. In addition, the DAO focuses on innovating through and driving advanced analytics in collaboration across the System.

Requirements

Extensive experience applying advanced analytics skills and techniques.
Ability to lead other data scientists to build and enable data analytics solutions.
Enterprise Architecture
Systems Design
Kubernetes
Cloud Computing (AWS/Azure/GCP)
Distributed Systems
Ray
Apache Spark
Dask
MPI
GPU Computing
AI/ML Infrastructure
LLM Operations
Performance Optimization
Cost Optimization
Network Architecture
Leadership
Strategic Planning
Stakeholder Management
Possession of or the ability to obtain and maintain national security clearance, which requires U.S. citizenship.
Candidates must undergo an enhanced background check and will be tested for all controlled substances prohibited by federal law, to include marijuana.

Responsibilities

Contribute in strengthening the relationships with key business stakeholders by translating business problems into analytics use cases and jointly prioritizing those use cases.
Develop accurate, robust, and equitable analytical models utilizing cutting-edge ML infrastructure and enterprise cloud architecture, AI/GenAI/LLM infrastructure and GPU-accelerated computing, enterprise-scale systems design and optimization, distributed systems (Ray, MPI, Apache Spark, Dask), Kubernetes operations and workload optimization (especially EKS), cloud platforms (AWS) and HPC environments, network architecture in complex corporate topologies, cloud cost optimization strategies and implementation, performance profiling and optimization techniques, strategic planning and roadmap development, stakeholder management across technical and non-technical audiences, and technical communication and translation of complex concepts.