Most careers stay in one layer of the stack. Mine has covered the investment problem, the research, the data, the calculations, the architecture and the platform, and I now lead the teams that connect them.
20+YEARS IN INVESTMENT MANAGEMENT, QUANT RESEARCH, DATA & TECHNOLOGY
2005CORPORATE ACTIONSBrown Brothers Harriman · Fixed Income
2008DATA ANALYSTAcadian · Investment Process & Database
2014MODEL INTEGRATIONAcadian · Quantitative Model Integration
2016VICE PRESIDENTAcadian · Investment Analytics & Data
2024DIRECTORFidelity · Quant Portfolio Research & Solutions
Module 01 · The problemREADY
I understand the investment problem.
Signal efficacy, factor behavior and micro-factors, forecasting inputs, portfolio and benchmark analytics, allocation and contribution, portfolio construction, trading and compliance.
RESEARCHPORTFOLIOTRADINGRISK
$backtest --signals QSG,CSFB→ passed signals integrated into the forecast model
$standardize --allocation --contribution→ consistent RFP and consultant analytics
Module 02 · The dataREADY
I understand the data underneath it.
Market, fundamental, security, pricing, corporate-action, benchmark, index, factor, risk, ESG and alternative data, and the vendors behind them. Quality, lineage, master data and classification managed as controls.
DATAREFERENCEQUALITYGOVERNANCE
$match --company-name --method levenshtein→ entities resolved across spelling variants
$classify --security-type --country --region→ one firm-wide taxonomy
$scrape --beautifulsoup --scrapy→ custom feeds where no vendor had the data
$monitor --ataccama --lineage→ zero-defect goal, owned and measured
Module 03 · The technologyREADY
I know how to turn it into technology.
Data models, repositories and pipelines: Python, SQL, data abstraction, ETL/ELT, change-data capture, APIs, distributed processing, partitioning and caching, up to full quant research platforms.
PYTHONSQLARCHITECTUREPLATFORMS
$migrate restricted_list constraints --from files --to sql→ optimizer inputs under control
$qml materialize --cache→ shared, reusable research datasets
Module 04 · The leadershipREADY
I lead where those disciplines meet.
At Fidelity I own the strategy for the Quant Research Platform. At Acadian I rose from data analyst to Vice President across analytics, research, data architecture and investment-process technology.
PRODUCTSTRATEGYLEADERSHIPEXECUTION
$career --from 2008→ data analyst · Acadian
$promote --2016→ vice president, investment analytics & data
$promote --2024→ director · Fidelity quant research platform
$align researchers pms engineering→ one roadmap, prioritized by researcher impact
Topics I like to discuss
Quant research platforms, research infrastructure, investment data, and the problems where they meet.