Quantitative Equity Analyst

About Lumida

Lumida is a digital-first wealth advisor for people who want access to the same opportunities as institutions — public markets, private deals, and alternatives.

Our mission is to transform wealth management — combining investment excellence and the best of trust and estate planning in a modern, tech-first experience.

Our team shares the same mindset as our clients — curious, original, and driven to build what doesn’t yet exist.

Recognized for our differentiated, non-consensus insights, Lumida is reshaping how modern investors build and enables its clients to invest beyond the ordinary.

Role Overview

We are seeking a Quantitative Equity Analyst with strong mathematical depth, scientific rigor, and a passion for systematic investing. You will build backtests, develop quantitative models, analyze factor behavior, research new alpha strategies, and support both long-only and long/short equity strategies.

If you are part data scientist, part engineer, and part investor — this role is for you.

Key Responsibilities

Quant Research & Backtesting:

• Build, test, and validate systematic equity strategies across asset classes including U.S. large & SMID-cap, global markets, commodities and digital assets.

• Run backtests and market studies: factor signals, technical indicators, statistical hypotheses, and cross-sectional regression models.

• Evaluate statistical significance, robustness, decay, and real-world execution frictions.

Model Development:

Develop and refine models for:

• Alpha generation

• Portfolio construction & allocation

• Position sizing & optimization

• Risk forecasting and hedging

• Performance attribution

• Implement models in Python (Pandas, NumPy, scikit-learn, statsmodels).

Methodology & Tooling Enhancement:

• Stay current with academic and industry research on factor models, risk premia, and machine learning.

• Improve Lumida’s backtesting infrastructure, factor libraries, and experimental workflows.Draft captions, hooks, scripts, and written content with sharp humor and clarity.

Documentation & Reporting:

• Produce clear research documents, whitepapers, dashboards, and presentations.

• Communicate insights with precision and strong writing skills.

Ideal Candidate

Education:

• Strong quantitative background: Mathematics, Physics, Statistics, Economics, Computer Science, or related fields.

• Master’s degree or higher preferred, or CFA

Technical Skills:

• Strong proficiency in Python (required).

• Experience with R, MATLAB, SQL, Excel, and standard data science/ML libraries (mlfinlab, pyfolio, backtrader etc).

• Strong understanding of: Probability and statistics, Linear algebra & convex or non-linear optimization, Machine learning

Quantitative & Finance Knowledge:

Familiarity with:

• Factor investing (value, earnings-yield, momentum, volatility, etc.)

• Risk management frameworks (Developing market-neutral, beta-neutral strategies, optimal hedging and position sizing frameworks)

• Portfolio theory (mean-variance, risk parity, optimization - eg: Black-Litterman, Markowitz, Treynor-Black models).

• Backtesting methodologies and empirical finance (Eg: Factor Decay and Turnover analysis, Regime analysis)

Mindset & Culture Fit:

• Highly analytical and intensely curious — always asking “why?”

• Problem solver with a scientific and hypothesis-driven approach.

• Strong writing and communication skills.

• Enjoys deep work, complexity, and rigorous thinking.

• A Leader, Collaborative, and thrives in a fast-paced, high-talent environment.

• Interest in markets, quantitative investing, technology, and data.

What We Offer:

• A front-row seat in building next-generation quantitative capabilities at a fast-growing, AI-native RIA.

• Direct collaboration with the CIO and the investment team.

• Opportunity to publish internal whitepapers and drive strategic research directions.

• Above market compensation with long-term growth upside.