- Company Name
- Paddle
- Job Title
- Lead Product Scientist
- Job Description
-
Job Title: Lead Product Scientist
Role Summary:
Lead Product Scientist drives the development and execution of product analytics and experimentation at Paddle, bridging product strategy and data science. The role designs experimentation frameworks, quantifies feature impact, and transforms complex data into actionable insights for cross‑functional teams.
Expectations:
- Establish and scale a product science function, managing a product scientist and setting analytical standards.
- Own end‑to‑end analytics workflows: hypothesis definition, experiment design, data execution, analysis, and storytelling.
- Influence product direction with data‑derived evidence and champion an experimentation culture.
Key Responsibilities:
- Partner with Product Managers, Designers, and Engineers to define success metrics and ensure every feature is measurable.
- Design and standardize experimentation processes, including A/B‑testing playbooks, templates, and guidance for consistent, reliable studies.
- Lead analysis of experiments and quasi‑experiments, quantifying product impact and identifying improvement opportunities.
- Explore user behaviour data to surface insights that inform prioritisation, conversion, and retention strategies.
- Build analytical frameworks, dashboards, and models that support product strategy and decision‑making.
- Collaborate with Analytics Engineering to maintain reliable, well‑documented datasets and metrics.
- Coach and mentor the direct report, providing guidance on analytical methods, communication, and stakeholder management.
- Translate complex data into compelling narratives that influence product direction.
- Contribute to a culture of experimentation, helping teams frame hypotheses, interpret results, and accelerate learning.
Required Skills:
- 5+ years of product analytics and experimentation experience, ideally in fintech or payments.
- Expertise in statistical methods, causal inference, and experiment design.
- Fluency in SQL and Python for data exploration, modelling, and analysis.
- Strong communication skills with the ability to tell data‑driven stories to non‑technical audiences.
- Proven ability to collaborate cross‑functionally, influencing decisions through evidence and clarity.
- Passion for building scalable experimentation frameworks and enabling self‑serve analytics.
- Commercial mindset: connecting analytical results to customer value and business outcomes.
- Experience mentoring others and growing a product science function.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Statistics, Data Science, Computer Science, Engineering, or related quantitative field (advanced degree preferred).
- Relevant certifications in analytics, statistics, or data science are advantageous but not mandatory.