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Senior Data Scientist - Product Data

principalhybridLondon, GBScore undefined/100today
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Description
Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. Role Purpose We're looking for a Senior Data Scientist (L5) to join our Product Data team collaborating with our Product Analysts to build deep understanding of how users engage with our AI-native products through conversation and interaction data . You will work at scale with unstructured text data - prompts, model outputs, user edits, feedback - to discover patterns that explain user behaviour and directly shape what we build next. The focus is on semantic analysis of conversations : understanding intent, identifying failure modes, detecting successful interaction patterns, and translating these insights into specific product improvements. This is hands-on analytical work, not infrastructure-focused. We need someone who can analyse messy conversation data, discover non-obvious patterns, and connect those findings to product decisions that move the needle on adoption, retention, and cost efficiency. For exceptional candidates with track records of leading analytics in AI-driven environments, we are open to hiring at Principal level, where you would set the broader data science agenda and mentor the existing analytics team. What You'll Do Build product evaluation frameworks Define how we measure "good" for AI-na
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