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Senior Data Scientist - Product Data
SynthesiaWebsite
Full-time
Senior
hybrid
Europe
Posted 23 hours ago1 views
Job 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-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.
WHAT WE'RE LOOKING FOR
EXPERIENCE
- Significant experience (ideally 5+ years) in Data Science, Product Analytics, or a similar role with conversation, chat, or dialogue data at scale. You have actually analysed large volumes of user text (prompts, messages, feedback, reviews) to discover patterns that shaped product decisions.
- Experience working closely with Product and Engineering teams.
- Hands-on experience working with AI/ML-driven products (LLMs, ranking, generation, recommendations). You have worked on these systems, not just alongside them.
SKILLSET
- Strong applied modelling skills: regression, classification, clustering, survival analysis, or similar. You pick the method that fits the question, not the other way around.
- Deep expertise in experimentation design and statistical inference. You can design a valid experiment, choose the right test, and explain the results to a non-technical audience.
- Fluency in SQL and Python. Comfortable working with large, messy, high-dimensional interaction data and building reproducible analysis pipelines.
- Ability to design pragmatic evaluation metrics where ground truth is fuzzy.
- Familiarity with modern data stack tools: dbt, Snowflake, Hex, Omni, Looker, or similar.
- Clear communicator who can influence without authority.
NICE TO HAVE
- Experience with product analytics platforms (e.g. Amplitude, Mixpanel, or similar).
- Experience working with NLP, text analytics, or unstructured data at scale.
- Experience building semantic search, relevance scoring, or interaction-based evaluation systems.
- Familiarity with prompt analytics, embedding-based analysis, or clustering user behaviour.
- Track record of influencing product strategy at a senior level, not just delivering analyses.
- Experience mentoring or technically leading other analysts or data scientists.
OUR CULTURE
At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best wor…
AI Stack
Skills Required
RAG
NLP
Data + AI
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