synthesia - Senior Data Scientist - Product Data
Requirements
• 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. • 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. • 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. • 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 work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here. https://www.notion.so/How-we-work-at-Synthesia-f794caa72f8446efb6be22b551ce0fa2?pvs=21
Responsibilities
• 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.
Benefits
• You will be compensated well with a generous salary and equity • Flexible, remote-friendly role for team members working in UK/Europe • You get 25 days of annual leave + local holidays • Regular team offsites where you’ll get to collaborate with the product & engineering team in person • Work from home budget • "Work from anywhere" up to 60 days per year • Generous referral scheme of up to $10,000 USD for each successful referral
Apply in one click
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT