Alternative Data Quant Jobs (NOW HIRING)

AWS is seeking an experienced Principal Solutions Architect to join the Worldwide Financial Services Industry (FSI) Business Unit as a Capital Markets Industry Specialist, with deep expertise in data and analytics, generative AI, and high performance compute. This is an ideal role for someone who has built large-scale data platforms, quantitative research infrastructure, or machine learning systems in capital markets, and who is ready to bring that expertise to hedge funds, asset managers, quantitative trading firms, and broker-dealers as they modernize their research and analytics capabilities on AWS.
In this role, you will serve as a core technical leader on the Capital Markets Industry Specialist team, working directly with quantitative hedge funds, systematic asset managers, multi-strategy funds, broker-dealer research teams, and quantamental research shops to architect and accelerate their migration of data-intensive workloads to AWS. You will engage at the intersection of deep technical expertise and business strategy, helping customers understand how AWS can transform their quantitative research, alternative data integration, backtesting infrastructure, and generative AI-powered investment workflows

This role requires hands-on experience building in AWS, with a strong understanding of how to architect scalable, performant, and cost-effective solutions for the most demanding analytical workloads in capital markets.
Role and Responsibilities
Design and architect AWS solutions with a specific focus on data and analytics, generative AI, and high performance compute for capital markets customers, collaborating with AWS Business Development, Partner, and account teams to help hedge funds, asset managers, quantitative trading firms, and broker-dealers migrate to AWS.
Serve as the primary technical subject matter expert for quantitative research infrastructure on AWS, including data lake and lakehouse architectures, alternative data integration pipelines, backtesting and simulation frameworks, portfolio optimization engines, and risk analytics platforms.
Architect solutions for large-scale data ingestion, transformation, and analytics workloads, including real-time and batch processing of market data, fundamental data, alternative data (satellite imagery, NLP on earnings calls, credit card transactions, web scraping), and ESG datasets, leveraging services such as Amazon S3, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Athena, and AWS Lake Formation.
Design and implement generative AI and machine learning solutions for quantamental research, including large language model (LLM) fine-tuning for financial document analysis, retrieval-augmented generation (RAG) architectures for research automation, sentiment analysis on news and social media, and agentic AI workflows for autonomous research and trading signal generation, leveraging Amazon Bedrock, Amazon SageMaker, and AWS Trainium/Inferentia.
Architect high performance compute (HPC) environments for computationally intensive workloads such as Monte Carlo simulations, options pricing, portfolio optimization, and quantitative backtesting, leveraging Amazon EC2 (compute-optimized and memory-optimized instances), AWS ParallelCluster, AWS Batch, and Amazon FSx for Lustre.
Engage directly with senior technical and business leaders at hedge funds (multi-strategy, long/short equity, quantitative, systematic macro), asset managers (active and passive), quantitative trading firms, and broker-dealer research teams to understand their data, analytics, and AI/ML requirements and develop compelling AWS-based solutions.
Develop and demonstrate technical feasibility through proof-of-concepts, prototypes, and reference architectures tailored to quantitative research and analytics workloads, including hands-on implementation of data pipelines, machine learning models, and HPC clusters on AWS.
Help customers evaluate and migrate their most data-intensive and compute-intensive workloads to AWS, including quantitative research platforms (e.g., Jupyter, RStudio, MATLAB), backtesting frameworks (e.g., Zipline, Backtrader, QuantConnect), and portfolio management systems, while addressing data governance, lineage, and compliance requirements specific to asset management and broker-dealer research.
Serve as a thought leader and evangelist for AWS in the capital markets data and analytics space, contributing to AWS blogs, whitepapers, reference architectures, and speaking at industry events such as Battle of the Quants, QuantMinds, and AWS re:Invent.
Capture and share best practices and insights internally and with partners and customers, building a repeatable playbook for data, analytics, GenAI, and HPC workload migration to AWS across hedge funds, asset managers, and broker-dealers.
Identify customer requirements and provide structured feedback into AWS service teams to influence the roadmap for data, analytics, AI/ML, and HPC services relevant to capital markets quantitative research.
Build trusted advisor relationships with senior executive stakeholders, as well as quantitative researchers, data scientists, data engineers, and infrastructure architects across the capital markets ecosystem, including CTOs, heads of quantitative research, and chief data officers at hedge funds, asset managers, and broker-dealers.
Think strategically about the evolution of quantitative research and investment technology, including the modernization of on-premises research infrastructure, the integration of alternative data and generative AI into investment workflows, and the emergence of agentic AI for autonomous trading and research.