Senior Data Scientist - Product Data

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.

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.

Build product evaluation frameworks Define how we measure "good" for AI-native features beyond traditional funnels. Build semantic frameworks to categorise user intent, model behaviour, and interaction patterns - e.g., "What types of prompts succeed vs. fail? What causes users to retry? What signals indicate satisfaction?" Design feedback loops that connect prompts, model outputs, user behaviour, and downstream outcomes. Define, track, and own the metrics that leadership and product teams depend on for a shared, trustworthy view of what is working and what is not. Analyse user–AI interactions at scale Work with large conversation datasets to deeply analyse how users interact with AI features (prompts, edits, retries, acceptance, abandonment). Identify failure patterns, unnecessary iteration loops, and the characteristics of successful interactions. Build models that answer the questions dashboards cannot: what predicts a good user outcome, what drives failure, and where the highest-leverage improvements are. Shape the product roadmap Turn findings into specific, prioritised recommendations that feed directly into the product roadmap. Partner with Product and Engineering to evaluate iterations quickly and close the loop between insight and action. Run experiments end-to-end: design, instrument, analyse, and translate results into clear product decisions. Drive growth and financial metrics Link AI feature usage to activation, retention, expansion, and cost efficiency. Help answer questions like: Which interactions create durable value? Where are we over-spending compute for low user impact? Build infrastructure and raise the bar Build pipelines, dashboards, and self-serve analytical tools that make insight accessible and trustworthy across the organisation. Mentor a team of product analysts, raising the technical bar and introducing new methods where they add real value.

Experience Significant experience (ideally 5+ years) in Data Science, Product Analytics, or a similar role with conversation, chat, or dialogue