Innovent Capital Group | Remote (United States) | Full time

$200,000 base salary, full benefits, performance pay, and participation in the strategies you build

About Innovent Markets Innovent Markets is the public-markets investment operation inside Innovent Capital Group. We invest our own capital. There are no outside investors, no fundraising cycle, no client reporting, and no investment committee. The mandate is to build an AI-native trading desk and make our public-markets investing systematic: run our Principal's research process across the entire market, on a custom stack with institutional data and a frontier AI reasoning layer over it, which you work with Innovent to procure and implement.

The trading team is two people. The Principal allocates capital and makes investment decisions. This role builds and runs the platform that produces and informs those decisions. Both of you sit within a larger Innovent investment team that also runs a seed fund, a portfolio of private companies, and an in-house studio that builds new ventures.

The role We are hiring one person to build and run our public-markets quantitative platform from scratch. This is a founding technical seat: you will select the stack, stand it up, and then use it to originate investment ideas.

The role combines quantitative research, software engineering, and investing. It reports directly to the Principal.

What you will build An end-to-end research pipeline that takes institutional market data and produces defensible investment ideas:

1. Institutional data: Bloomberg / LSEG / S&P Capital IQ
2. AI reasoning: frontier LLM
3. NL quantitative engine: natural-language screening across the equity universe
4. Technical scanner: price, volume, RSI, MACD, moving averages, relative strength, options
5. Fundamental scanner: FCF, EV, growth, margins, estimates, balance sheet
6. Narrative engine: news, transcripts, filings, analyst research
7. Agent: explanation of why an opportunity exists

A representative task: translate an investment observation into a screen run across the US equity universe - size band, moving-average structure, momentum and volume conditions, drawdown from highs, FCF and growth thresholds, short interest - rank the survivors, then read their recent earnings calls and explain the dislocation in each.

Tools may include Bloomberg with ASKB, BQL and BQuant. Whether the reasoning layer sits inside the terminal or on top of it, via LSEG or S&P into a frontier model, is an open architectural decision that you will help steer.

1. Responsibilities50% Quantitative research and data science. Python, data pipelines, screens, factors, backtests, and models validated against live markets.
2. 30% Investing. Originating ideas, forming and defending theses, revising them against results.
3. 20% Engineering and infrastructure. Standing up the platform, integrating market-data APIs and AI tooling, shipping working systems.

1. ProgressionFirst 90 days. Select and stand up the stack. Get institutional data flowing. Reproduce the Principal's existing research process as a repeatable system and ship a first production screen.
2. Months 4 to 12. Move from screening to strategy: build, test and document systematic approaches with statistical rigor, and bring investable ideas forward for funding.
3. Years 2 to 4. Take responsibility for a dedicated capital sleeve. This is the intended path for the role, not a guarantee.

Working alongside the Innovent team Innovent Markets is one of many projects undertaken by our in-house team of entrepreneurs-in-residence. We operate an internal incubator, manage multiple funds, and advise and work on behalf of portfolio companies across many sectors and stages. This role will have direct exposure to this work, and will have the opportunity to contribute to new businesses and investment decisions from their uniquely qualified position analyzing public markets and as a user of trading tech.

1. Incubation. Innovent builds new companies in house. You will see concepts early and can engage with the ones that touch markets, data, or AI infrastructure.
2. Technology evaluation. The firm assesses tools, data vendors and AI platforms across the whole portfolio, not only for this desk. Your judgment on those decisions will be used beyond your own stack.
3. Business development in market-related opportunities. Where the work surfaces something commercial - a product, a data relationship, a venture worth starting - we have a methodology and toolset to bootstrap a company very quickly. Our Principal has founded and operated trading-related technology companies, and ideas of that kind are exactly the kind of startups we want to found.
4. Entrepreneur-in-residence. Equity participation in projects and new startups where you are a primary contributor.

1. Requirements 1 to 5 years of experience and/or education in quantitative research, investment technology, and software engineering, with a focus and drive to understand markets, using technical analysis, trading strategies, and data analysis.
2. Advanced Python, SQL, and general software development proficiency. You will build and maintain production software, not only analysis scripts. Rails also a plus.
3. Direct experience with market data: fundamentals, pricing, estimates, filings and corporate actions, including their common data-quality failures. Experience and proficiency with Bloomberg and related stack a plus.
4. Statistical rigor, including practical experience identifying and avoiding overfitting, and a clear understanding of the limits of backtests.
5. Demonstrated depth of interest in public markets, including views on specific securities you can support with evidence.

1. Preferred qualifications Current or recent experience at a bank, asset manager, fintech, financial data vendor, or quantitative investment firm.
2. Alternatively, a motivated and exceptional student who builds their own trading and research tools, trades their own book, or demonstrates aptitude in the field will be considered.
3. Hands-on familiarity with Bloomberg, FactSet, Capital IQ, LSEG or MSCI data, including building against those platforms. Personal curiosity and proficiency with modern LLMs, agentic computing and coding assistants, and competency with data storage and retrieval, interface design, and other development tools a must.
4. Personal work product: research, tooling or systems you started and built yourself that you can show us, walk us through the way you made it, and what it's used for.
5. Comfort operating with a broad mandate and minimal existing infrastructure. Self motivated and inherently curious and intellectually honest individual with no time to waste.

An MFE, PhD, or prior experience at a name-brand fund is not required and is not weighted heavily.

1. Compensation and benefits$200,000 base salary.
2. Full benefits , including medical, PTO and 401k.
3. Performance and participation-based bonus pay, details to be described.

How to apply Send a resume along with a short written response (a few paragraphs) covering one market idea you believe is true, how you would test it, and what data you would need. Links to work you have built are welcome and carry more weight than a cover letter.