Staff Data Scientist — Growth & Experimentation Lead

Preply; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Learning & Development budget

Equity package

Health insurance

Relocation package to London hub

Flexible time for self-development

Job summary

Preply in London seeks a Staff Data Scientist to own the data strategy for a key product domain and partner with Directors and VPs across Product, Engineering and Business to shape long-term strategy grounded in rigorous measurement.

The role emphasizes experimentation, forecasting and causal inference with a focus on growth, retention and marketplace dynamics. Hybrid work and a strong data culture enable impact at scale.

Qualifications

• Proven track record shaping product or business strategy at scale.
• Strong expertise in experimentation and causal inference under imperfect conditions.
• Deep understanding of subscription and marketplace economics.

Responsibilities

• Define key value drivers and translate into decision frameworks for leadership.
• Design long-term measurement and experimentation strategies for growth domains.
• Lead large-scale experiments and evaluate outcomes impacting business value.
• Investigate customer behavior and retention dynamics to drive long-term value.
• Own impact modeling, forecasting and risk assessment for major initiatives.
• Govern key metrics and align analytics with company objectives.
• Partner with product, engineering and data leaders to raise analytical standards.
• Drive cross-domain initiatives like LTV modeling and pricing frameworks.

Skills

SQL

Python

A/B testing

Causal inference

Education

Master's degree or PhD in a quantitative field

Tools

Tableau

Looker

Power BI

Amplitude

Job description

Preply in London seeks a Staff Data Scientist to own the data strategy for a key product domain and partner with Directors and VPs across Product, Engineering and Business to shape long-term strategy grounded in rigorous measurement.

The role emphasizes experimentation, forecasting and causal inference with a focus on growth, retention and marketplace dynamics. Hybrid work and a strong data culture enable impact at scale.