Interim Senior Data Scientist

Go Fractional; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Growth & wellness budgets

Sponsored office visits

Company offsites

Premium health coverage

Job summary

Goodnotes is seeking a Senior Product Analytics Lead to shape the data-backed future of a new AI-native product line in London. You’ll define metrics, build experimentation, and ensure instrumentation drives revenue. You’ll empower teams to self-serve insights and reduce bottlenecks through scalable frameworks.

You’ll work onsite at the Paddington office on a 1-year fixed-term contract, embedding with Steven and a fast-moving team to ship the right early bets with precision.

Qualifications

• Significant experience of product analytics in a PLG SaaS, marketplace, or transactional environment.
• Deep experimentation experience and understanding of failure modes.
• Strong instrumentation and data governance instincts with event taxonomy knowledge.
• Experience in AI-native product orgs and building scalable insight workflows.

Responsibilities

• Define success metrics, size opportunities, and connect product metrics to revenue.
• Design and standardize experiments, moving decisions from opinion to evidence.
• Own instrumentation and measurement quality with clear tracking plans.
• Turn behavioral insights into actionable recommendations for product and revenue.
• Build self-serve frameworks so PMs read results confidently and teams act quickly.

Skills

Product analytics

SQL

Python

Experiment design

A/B testing

Instrumentation

Data governance

AI-native product

Tools

dbt

Looker

Hex

LightDash

Job description

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:

• 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.

• 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.

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

• 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.

• 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.

• 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.

• 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 AI-native product orgs. You've gone past chatting with Claude/ChatGPT to building proactive agentic workflows that scale insights discovery and delivery with minimal human intervention.
• Strong SQL plus Python and/or R – you write queries and build analyses yourself, regularly.
• A track record of building frameworks others adapt and extend. You make teams smarter, not just yourself heard.
• Excellent communication, you adapt your altitude to the audience
• Comfort operating in ambiguity with autonomy.
• Familiarity with dbt, semantic/BI layer, and governed self-serve stacks (Hex, LightDash, Looker, or similar)

Preferred:

• Experience in productivity SaaS businesses
• Familiarity with dbt, semantic/BI layer, and governed self-serve stacks (Hex, LightDash, Looker, or similar)
• Causal inference methods beyond A/B tests
• Experience implementing experimentation platforms (e.g. Statsig, Eppo or in-house)
• Familiarity with product analytics tools (Amplitude, Mixpanel, Firebase, or similar)

The interview process:

• Talent Intro Call: A conversation with our Talent Acquisition team to dive into your experience, what motivates you, and why you're interested in joining Goodnotes.
• Hiring Manager Interview: A deeper dive into your professional background, your preferred ways of working, and the specific impact you'll have within the team.
• AI Literacy: As an AI-first company, you'll meet with one of our AI champions to discuss your curiosity, understanding, and practical use of AI tools in your daily workflow.
• At-Home Case Study: A take-home exercise to help you prepare for your live Role-Specific Assessment - you'll receive this in advance so you can work through it at your own pace.
• Role-Specific Assessment: A live session building on your case study, focused on the core technical and functional skills required for the role. This is your chance to walk us through how you tackle real-world challenges. This will take place onsite in our Paddington office.
• Values Based Panel Interview: A conversation with 2-3 team members centered on our company values. We'll discuss past experiences to see how your approach aligns with our culture.

What's in it for you:

• Customized Growth & Wellness Budgets: We provide dedicated stipends for the things that keep you at your best, including noise-canceling headphones for deep focus, professional training, personal development, and health and wellness activities.
• Global Connectivity: While we embrace flexible work, we love seeing each other. We provide sponsored visits to our beautiful office locations to foster face-to-face collaboration.
• Company-Wide Offsites: Once a year, we gather the entire global team in person to celebrate our wins, align on our vision, and build lasting connections.
• Comprehensive Health Coverage: Your well-being is our priority. We offer premium medical insurance for you and your dependents to ensure peace of mind for your whole family.