Senior Data Scientist - Stealth AI (12-months Fixed-Term Contract – Renewable)

About this role

At Goodnotes, we believe that every individual holds untapped potential waiting to be unleashed. By reimagining the way we interact with information, we’re merging human creativity with the breakthrough capabilities of AI. Our renewed vision and mission drive us to create the best medium for human and AI collaboration, empowering users to explore new dimensions of productivity, creativity, and learning. Join us on this journey as we transform digital note-taking into an inspiring and innovative experience.

Our Values

Dream big

• Be visionary, strategic, and open to innovation

Build great things

• Work in service of our users, always improving and pushing higher

Operate like an owner

• Take responsibility with bold decision-making and bias for action

Win like a sports team

• Be trusting and collaborative while empowering others

Learn and grow fast

• Never stop learning and iterate fast

Share our passion

• Share ideas and practice enthusiasm and joy

Be user obsessed

• Empathetic, inquisitive, practical

About the role

Think of this as a startup within Goodnotes. You'd be embedded in one of our 0-1 product bets -working directly with Steven, our founder, and a small, fast-moving team building something genuinely new inside Goodnotes' AI-native product line. No legacy roadmap, no established playbook. You're helping write the first one.
You'll be the analytical partner for that product: quantifying how features move the business, building the experimentation and instrumentation that lets the team measure impact from day one, and turning ambiguous, undefined problem spaces into clear questions and confident decisions. In a 0-1 environment, that clarity is the difference between shipping the right thing and guessing.
You'll combine analytical precision with product intuition - designing experiments, uncovering the behavioural drivers behind conversion, activation, and retention, and connecting product metric movements straight back to revenue.
Crucially, you won't just do the analysis. You'll build the frameworks, playbooks, and self-serve capability that let the product team answer their own questions — the kind that hold up when you're not in the room. Removing analytics as a bottleneck isn't a side goal here. It's part of the job.

• This role is based full-time onsite at our London (Paddington) office

• This role is a fixed term contract of 1 year

This is the role for you if you're excited to work on:

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Defining success metrics & sizing opportunities: Partner with GTM, Product, and Engineering to set success metrics, size opportunities, and connect product metrics to revenue — ensuring every team knows what "good" looks like.

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Building the experimentation practice: Design and analyze experiments with statistical precision, standardize how experiments run across squads, and help the organization move from opinion-driven to evidence-driven decisions.

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Owning instrumentation & measurement quality: Work with Engineering on tracking plans and event taxonomy so features are measurable before they ship, not retrofitted after.

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Turning behavior into insight: Run deep-dive analyses on funnels, cohorts, activation, and retention, and translate findings into actionable recommendations that drive product and business outcomes.

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Enabling teams to self-serve: Teach PMs and product leaders to read experiment results and governed reporting with confidence. Build frameworks others can adapt and extend — reducing the analytics team as a bottleneck.

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Shaping the agenda: Proactively surface the questions the product organization should be asking before they're asked, and know when a finding is sufficiently reliable to drive action.

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Defining good enough: knowing when a finding is sufficiently reliable to drive action, avoiding the trap of pursuing endless granular accuracy.

The skills you will need to be successful:

• Significant experience of product analytics in a PLG SaaS, marketplace, or transactional environment. You understand funnels, retention curves, user lifecycle, and how product metrics connect to revenue.

• Deep experimentation experience. You’ve designed and analysed experiments, and you know the common failure modes (peeking, underpowered tests, bad randomisation, metric gaming) and how to design around them.

• Strong instrumentation and data governance instincts. You’ve defined tracking plans, and worked with engineering teams on event taxonomy.

• Experience working in…