Research, design, and implement indices and systematic strategies across fixed income, equities, and derivatives — including structures such as covered calls, duration barbells, and factor portfolios
Build and maintain multi-asset allocation models, with attention to efficient replication, tracking error control, and transaction costs
Run rigorous backtests: no look-ahead, no survivorship, realistic costs. We don't publish a curve without out-of-sample replication
Help build the quantitative infrastructure of the team: reusable data pipelines, backtest frameworks, monitoring dashboards, and reporting workflows on Databricks. The goal is that every model you build outlives the project it was born in
Develop AI-powered tools for investment research: LLM-driven analysts on top of our internal datasets, agents for routine analytical tasks, copilots that compress the distance between question and answer for the whole team
Support the launch of new ETFs — from index methodology to interactions with index prodviders, market makers, administrators, and custodians
Read papers, replicate results, and tell apart what works from what looks like it works
What we're looking for
Bachelor's degree in a quantitative field: engineering, math, physics, statistics, economics, computer science, quantitative finance, or equivalent
3+ years in quantitative research, systematic asset management, risk, or adjacent roles
Strong Python (pandas, numpy, scipy). Comfort writing code that other people will read, run, and extend
Curiosity about — and ideally experience with — building production-grade analytical infrastructure: pipelines, jobs, dashboards, internal tools
Genuine interest in applying LLMs and AI agents to quantitative work, not just as users but as builders
Familiarity with the Brazilian market: NTN-B, IMA, Ibovespa, B3 derivatives, local ETF dynamics
A collaborative, constructive way of working: you ask for help when stuck, you offer help when others are, you document, you review code, you disagree clearly without making it personal, and you give credit generously
Ability to communicate quantitative results clearly — to portfolio managers, commercial teams, and ultimately to the end investor