Full Stack Data Scientist
Discovered
WorldwideremotePosted 21 days ago
Skill Required
Data-ScienceSEO-SpecialistAEO-SpecialistAnalyticsDigital-MarketingFull-Stack-Data-ScientistFull-Stack-Data-ScienceMid-Level-Full-Stack-Data-ScientistData ScientistContent MarketinganalyticalautomationdesigninganalyticsbuildingdesignGenerative AISEOandAIMachine LearningFulltime
Key highlights
- $30,000 - $50,000 annual salary depending on relevant skills and experience
- Level Required: not explicitly stated beyond 'demonstrated evidence of shipping where analysis changed what a business did'
- Key benefit: Best in-house SEO/AEO tools (ML/AI and data science tooling) built by top engineers
- Notable requirement: AI-native with portfolio of building side projects, agents, scrapers, or internal tools; build workflows not just prompts
- Notable requirement: High agency and builder's instinct weighted above everything including domain knowledge
- Time commitment: Fully remote, ideally overlapping GMT business day with ±4 hours flexibility
Role overview
Discovered Labs is an SEO/AEO agency built by a Stanford rocket scientist turned AI researcher and a demand gen marketer with experience scaling B2B SaaS companies to 8-figures in ARR. They serve hyper-growth B2B SaaS companies like Instantly, Granola, and Incident who want more leads and customers from Google and AI assistants. The agency uses AI automation, senior expertise, and full-stack approach across the entire funnel. This role is for a Data Scientist who owns the end-to-end loop of analysis, implementation, and reporting with their internal tooling.
Responsibilities
- Analyse data behind reports, strategy questions, client queries, technical issues and SWOTs — reconciling messy, incomplete sources into a clear picture of what's actually happening.
- QA via analysis - Is the rationale sound? Does the logic hold? Where's the unsupported claim?
- Run experiments. Design a falsifiable test, run it, and know when a result is noise rather than signal.
- Tie every finding to the client's business. You get to 'so what, and why does this matter to their revenue,' not just 'here's what the data says.'
- Own the report. Hand the client and the account strategist a decision-ready read the data, the diagnosis and the 'so what?' never a raw data dump.
- Optimise for answer engines (AEO). Make client content the source LLMs and Google AI Overviews cite, using their internal methodology and tools, as one surface among many.
- Execute, not just recommend. Implement findings yourself with internal tooling. Analysis only counts once shipped.
- Improve tactics and systems. Turn one-off findings into repeatable processes, and keep sharpening tooling and methodology behind how the system works.
- Communicate cleanly to clients, in writing and on calls, so output needs no translation.
Requirements
- Own the full loop end to end, analyse → explain → implement → report. Don't hand off execution: analyse it, build it, ship it.
- High agency and builder's instinct weighted above everything including domain knowledge. High agency covers execution and tempo; builder covers coding, AI workflows, and evolving methodology.
- Strong analytical and quantitative background: reconcile messy, incomplete data (GSC exports, rank trackers, partial client analytics not clean warehouse tables), design falsifiable experiments, and reason to root cause with evidence, never inventing cause where data is silent.
- Demonstrated evidence of shipping where analysis changed what a business did, and implemented at least part of it yourself. Insight without execution is wrong fit.
- AI-native and built something with it: side project, agent, scraper, or internal tool. Use AI tools daily as workflow multiplier (briefs, clustering, audit summarisation, scraping) and still own final validated output. Build workflows, not just prompts. Portfolio over claims.
- Experiment design and statistical rigour: design a test, run it, and know when result is noise.
- Systems thinking: improve the tactic and improve the system behind it, turning one-off finding into repeatable process.
- Commercial curiosity: get to 'so what?' unprompted, asking about client's business model and KPIs, and land every finding as business consequence (revenue, pipeline, cost) not just metric movement.
- Strong written and verbal English, ability to convey findings cleanly to non-technical stakeholders. Reports and QA rationale go straight to clients; analysis only counts once client understands and acts on it.
Nice to have
- Experience with SEO/analytics tooling (GSC, GA4, Ahrefs, crawler such as Screaming Frog). If don't have it, will ramp you — this is teachable.
- Client-facing experience, and commercial awareness.
- Familiarity with B2B SaaS.
Benefits
- $30,000 - $50,000 annual salary depending on relevant skills and experience
- Fully remote position. (Ideally flexible within roughly ±4 hours GMT)
- Room to grow. Small but growing, which means real opportunity, including a senior track with no people management required.
- Paid leave and autonomy to perform role.
- Best in-house SEO/AEO tools i.e. ML/AI and data science tooling, built by top engineers, at disposal.
Additional details
- About Discovered Labs: Stanford rocket scientist turned AI researcher and demand gen marketer with experience scaling B2B SaaS companies to 8-figures in ARR. Trusted by hyper-growth companies like Instantly, Granola, incident and other $10M+ ARR B2B SaaS companies.
- Three approach pillars: AI automation and workflows, Senior expertise, Full-stack.
- Role is fully remote. Working hours overlap GMT business day (flexible within roughly ±4 hours).
- This is not for you if: need heavy management or wait for perfect instructions; produce genius insights but don't ship them; lack experience and/or willingness to use advanced AI workflows; prefer to spend weeks performing audits before implementation; not comfortable with data and analytics.
- Highlights: If want to stand out, include a Loom showing a tool, system or deliverable built and proud of. We'd love to see it.
- How time is spent: 40% Implementing/shipping, 30% Analysing/diagnosing/measuring, 15% Reporting/client communication, 15% Building/evolving methodology.
- Culture: believe search has fundamentally changed; AI is force multiplier not crutch; speed and quality both achievable. Thrive if get energy from excellent work that makes clients successful, see gaps and fix them without being asked, move fast while proactively communicating. Won't thrive if need heavy management, wait for perfect instructions, or think SEO is still just about keywords and links.