About the Role:

You will research, design and build the risk, factor and performance analytics that power our platform - factor risk decomposition, performance attribution, exposure analytics and scenario testing, taking each one from an open research question through to a tested, documented, production analytic used by hedge funds and asset managers.

You will own analytics that sophisticated investment professionals rely on, which means the methodology has to hold up to scrutiny. We are looking for people with genuine quantitative aptitude, strong problem-solving instincts and a real interest in financial markets.

Website:

https://equitydatascience.com/

What You Will Work On:

1. Quantitative research:

- Investigating how a metric should be defined, testing candidate methodologies against real portfolio data, comparing our results against established industry approaches, and determining what is robust enough to ship into the product.

2. Risk models and factor analytics:

- Factor exposures and risk decomposition (factor vs. idiosyncratic/specific risk), betas, correlations, tracking error, and the integration of third-party risk model data alongside our own calculations.

3. Performance and attribution analytics:

- Multi-period return attribution across factors, sectors, countries, regions, market cap and custom groupings; Brinson-style and factor-based attribution; contribution analysis; realised vs. unrealised P&L breakdowns.

4. Exposure analytics:

- Gross, net and long/short exposure; lookthrough for fund-of-fund and basket structures; benchmark-relative exposure; concentration and diversification measures.

5. Scenario, stress and drawdown analytics:

- Historical and hypothetical stress scenarios, drawdown attribution, and what-if analysis on live portfolios.

6. Data integrity:

- Reconciling computed analytics against third-party and client-provided figures, and building the automated data-quality checks and monitors that catch a bad number before it reaches a user.

7. Analytics performance engineering:

- Making all of the above run fast over large holdings and returns panels through pre-aggregation, query tuning and caching, so results return interactively rather than overnight.

Responsibilities:

- 1. Take ownership of an analytics area end to end - understand the investment problem it solves, select the methodology, implement it, validate it, and maintain it in production.

- 2. Research and prototype new quantitative analyses, then productionise the ones that prove out, in Python, R and SQL - with tests, peer code review and written documentation of the methodology.

- 3. Own the correctness of the numbers. Investigate discrepancies between our analytics and third-party or client-provided figures, isolate the root cause, and explain it clearly to product, support and engineering colleagues.

- 4. Work with product managers and clients to turn what investment teams need into well-specified analytics.

- 5. Analyse and interpret a wide range of financial and statistical data - holdings, transactions, returns, factor models, market and reference data - to understand the problem and devise optimised solutions.

- 6. Identify bottlenecks and make the code leaner and faster; use engineering skills creatively to solve real-world investment problems.

- 7. Manage several research and delivery workstreams at once in a fast-paced environment, with an innovative and solution-driven approach.

- 8. Communicate well in writing. On this team, documenting how a metric is calculated matters as much as calculating it.

Qualifications - Required:

- 1. Bachelor's, Master's or MBA in a quantitative field such as Mathematics, Statistics, Finance, Economics or Engineering.

- 2. Proficiency in Python and/or R, plus working SQL - comfortable writing and optimising queries over large datasets.

- 3. Working knowledge of investment analytics fundamentals: return calculation and compounding, benchmark-relative performance, and the concepts behind factor exposure and performance attribution.

- 4. Knowledge of and genuine interest in financial markets, including the judgement to recognise when a number does not look right.

- 5. Clear written and verbal communication - able to explain a quantitative method to product and engineering colleagues who are not quants.

- 6. Good problem-solving skills and quantitative aptitude.

Qualifications - Preferred:

- 1. Hands-on exposure to multi-factor risk models - factor exposures, specific risk and risk decomposition.

- 2. Familiarity with performance attribution methodology - Brinson-Fachler style and factor-based attribution.

- 3. Exposure to portfolio construction, optimisation, or stress and scenario testing.

- 4. Experience with market and reference data vendors such as FactSet, Bloomberg or MSCI.

- 5. Experience with data visualisation tools, and an eye for presenting an analytic a portfolio manager can read in five seconds.

- 6. Progress towards CFA or FRM.

What We Offer:

- 1. An atmosphere of growth and opportunity, with real ownership of analytics used by sophisticated institutional investors.

- 2. A collaborative culture of curious, dedicated people who take the quality of their work seriously.

- 3. Ownership: you will be the named owner of analytics that ship in our platform, not a contributor to someone else's backlog.

- 4. The room to do actual research - time to test a methodology properly before it goes into the product.

- 5. A modern WeWork office with cappuccino on tap, pool tables and an energetic atmosphere.

- 6. An employee-friendly salary structure with strong benefits.