Data / Quant Engineer
Build the back-testing engine and portfolio-construction intelligence that every Arithmos claim depends on.
About Arithmos
Arithmos is building the retail layer for direct indexing. We turn a plain-English idea into a transparent, rule-based portfolio with institutional-grade backtesting and analytics. Our goal is to make structured investing accessible to millions.
The role
You will build the core intelligence layer of Arithmos. Every index, every backtest, and every performance claim depends on the accuracy and robustness of your work.
What you’ll do
Design and maintain backtesting infrastructure
- Historical simulations across equities
- Handling corporate actions, survivorship bias, rebalancing
Build portfolio construction logic
- Weighting systems (equal-weight, factor-based, custom rules)
- Constraint handling (sector, geography, liquidity)
Develop performance and risk metrics
- Volatility, drawdowns, Sharpe/Sortino
- Benchmark comparisons (S&P 500, custom benchmarks)
Improve data pipelines
- Pricing data ingestion
- Cleaning, validation, and consistency checks
Work closely with product
- Ensure outputs are accurate, explainable, and trustworthy
Requirements
- 3–8+ years in quant, data engineering, or systematic investing
- Strong Python (NumPy, Pandas)
- Deep understanding of portfolio construction, backtesting pitfalls, and financial data structures
- Experience working with large datasets efficiently
Nice to have
- Experience in hedge funds, asset management, or quant shops
- Knowledge of factor models or systematic strategies
- Experience building user-facing quant tools
Why this role is exceptional
- You define the credibility of the product
- Opportunity to build a retail-grade quant engine
- Work at the intersection of AI + investing + product
- High autonomy, high impact, high upside
Compensation & benefits
- Private healthcare
- 25 days holiday + bank holidays
- Top-tier equipment
- Flexible working
- Annual company offsite
How to apply
Upload your CV and a short note — no more than 200 words — on why this role in particular. If you have work you’re proud of that’s relevant (a product you shipped, a research paper, a backtest you ran), link to it. Two-stage process: screening call, then a take-home + technical deep-dive with the team you’d join.
