Quantitative Strategist (Fintech)

TL;DR

Quantitative Strategist (Fintech): Enhancing execution algorithms for cash equities and conducting quantitative research on market microstructure with an accent on order-book dynamics, venue analysis, and transaction cost analysis. Focus on building statistical and machine learning models for short-term price prediction and collaborating with technology teams to productionize research into low-latency trading systems.

Location: London, Greater London, England, United Kingdom

Company

hirify.global is a leading global investment banking, securities, and investment management firm founded in 1869.

What you will do

• Enhance execution algorithms for cash equities, such as VWAP and adaptive strategies.
• Conduct quantitative research on market microstructure, order-book dynamics, and transaction cost analysis (TCA).
• Build and maintain statistical and machine learning models for price prediction, fill-rate estimation, and market-impact modelling.
• Collaborate with technology teams to productionize research into low-latency trading systems.
• Perform back-testing, simulation, and live A/B testing of algorithm enhancements; define and track performance metrics.
• Partner with sales, trading, and client-facing teams to translate client feedback into research priorities.

Requirements

• Advanced degree (Master's or PhD) in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or related).
• 5+ years of experience in quantitative research related to execution/trading algorithms.
• Deep understanding of market microstructure concepts, order types, venue fragmentation, and market-impact models.
• Proven experience with statistical modelling, time-series analysis, and/or machine learning applied to financial data.
• Proficiency in working with large datasets (tick data, order-book snapshots).
• Excellent communication skills to convey complex quantitative concepts to both technical and non-technical audiences.

Nice to have

• Experience with equities execution algos in European or global markets.
• Understanding of regulatory frameworks relevant to algorithmic trading (MiFID II).
• Strong programming skills in Python.
• Ability to query data in kdb+/q.
• Familiarity with reinforcement learning or deep learning techniques applied to optimal execution problems.

Culture & Benefits

• Committed to fostering and advancing diversity and inclusion in the workplace.
• Offers training and development opportunities and firmwide networks.
• Provides benefits, wellness, personal finance offerings, and mindfulness programs.