Hands-On Quant Team Lead — Madrid (Hybrid)

Allen Recruitment; Madrid; Hybrid; EUR 60,000 - 90,000; Full time

Job summary

Allen Recruitment in Madrid is seeking a hands-on Quant Team Lead to combine deep mathematical modelling with day-to-day leadership of a small analytics team. You’ll build models, validate assumptions, work in Python + SQL, and set the technical standard while coaching others under tight deadlines.

Location: Madrid (Hybrid) with typically 3 days in-office. You’ll lead analytics delivery on live valuation and portfolio analysis, mentor analysts, improve models, and turn messy datasets into

Qualifications

• Strong mathematical/quant foundation.
• Experience in quantitative finance, risk, valuation or portfolio analytics.
• Strong Python (pandas) and SQL skills.
• Leadership experience: team lead or mentoring.
• Ability to deliver decision-grade analysis under deadlines.

Responsibilities

• Lead analytics delivery on live valuation and portfolio analysis (hands-on modelling + ownership).
• Mentor analysts with modelling best practices and quality control.
• Improve quantitative models used for valuation, forecasting, and risk/controls.
• Turn messy datasets into reliable outputs with QA and pipelines.
• Run scenario analysis and stress testing; explain drivers of value and risk.
• Present findings to senior stakeholders and defend assumptions.
• Improve team processes: templates, documentation, automation, governance.

Skills

Math/Stats/Physics

Quant Finance

Python

SQL

Leadership

Mentoring

Tools

Pandas

Job description

Allen Recruitment in Madrid is seeking a hands-on Quant Team Lead to combine deep mathematical modelling with day-to-day leadership of a small analytics team. You’ll build models, validate assumptions, work in Python + SQL, and set the technical standard while coaching others under tight deadlines.

Location: Madrid (Hybrid) with typically 3 days in-office. You’ll lead analytics delivery on live valuation and portfolio analysis, mentor analysts, improve models, and turn messy datasets into