Full Stack Data Scientist

About Discovered Labs

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.

We’re trusted by hyper-growth companies like Instantly, Granola, incident and other $10M+ ARR B2B SaaS companies who want more leads and customers from both Google and AI assistants like ChatGPT.

We do this through a mixture of content marketing, technical optimisations and off-site brand amplification (PR, backlinks, Reddit marketing). View recent case studies at .

High-level, our approach rests on three things:

• AI automation and workflows. We build marketing programs the way you’d build a product. Rather than burn hours a week on repetitive tasks, our in-house team of ML engineers builds workflows with custom tooling so we can focus on the high-impact work.

• Senior expertise. Everything is managed and elevated by a team of experts, so it’s done to a high standard and differentiated. We don’t let automation get in the way of quality.

• Full-stack. SEO and AEO rarely work as isolated tasks. We work across the entire funnel and multiple surfaces so we own the outcome and clients win.

If you want to stand out, include a Loom showing a tool, system or deliverable you’ve built and are proud of. We’d love to see it.

This role

You analyse the data, work out why performance is moving and turn the result into a report the client can act on. You implement the fixes and improvements yourself with our internal tooling.

“DS who owns data → analysis → decision → implementation end to end”

Concretely, that means you:

• Analyse the 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 our internal methodology and tools, as one surface among many.

• Execute, not just recommend. You implement the findings yourself with our internal tooling. The analysis only counts once it's shipped.

• Improve the tactics and the systems. Turn a one-off finding into a repeatable process, and keep sharpening the tooling and methodology behind how the system works.

• Communicate it cleanly to clients, in writing and on calls, so your output needs no translation.

This role is fully remote. Ideally your working hours overlap the GMT business day (we’re flexible within roughly ±4 hours).

This is not for you if:

• You need heavy management, or you wait for perfect instructions before acting. If you spot a broken process, we expect you to fix it.

• Produce genius insights but don’t ship them. Insight with no execution is exactly the failure mode we screen for. The analyst who gets on the tools is the one we want.

• Lack experience and/or willingness to use advanced AI workflows.

• Prefer to spend weeks performing audits before implementation. B2B SaaS clients expect results fast and everything needs to tie to commercial impact.

• Not comfortable with data and analytics. We want someone who can diagnose the why behind performance and back it with evidence.

What’s in it for you

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. We’re small but growing, which means real opportunity, including a senior track with no people management required.

• Paid leave and autonomy to perform your role.

• The best in-house SEO/AEO tools i.e. ML/AI and data science tooling, built by top engineers, at your disposal.

How you’ll spend your time

A rough split; it flexes month to month with what each account needs.

• 40% — Implementing and shipping. Producing and shipping the week’s work across your accounts: technical fixes, on-page, content refreshes, internal linking, off-page and AEO, all verified at source before it goes out.

• 30% — Analysing, diagnosing and measuring. Getting into the numbers to understand cause and effect, designing and running experiments, and measuring what actually moved after a change.

• 15% — Reporting and client communication. Owning the client-facing report, being present on calls to walk through the diagnosis, staying close in day-to-day client Slack, and giving the account owner the read behind the story they lead.

• 15% — Building and evolving the methodology. Sharpening your own skills as search and AI assistants keep changing, and constantly improving the tooling, templates and process behind how the team works.

Requirements

• This role owns the full loop end to end, analyse → explain → implement → report. You don't hand off execution: you analyse it, you build it, you ship it.

• High agency and a builder's instinct are the two traits we weight above everything, including domain knowledge. High agency covers execution and tempo; builder covers coding, AI workflows, and evolving the methodology.

• Strong analytical and quantitative background: you reconcile messy, incomplete data (GSC exports, rank trackers, partial client analytics not clean warehouse tables), design falsifiable experiments, and reason to a root cause with evidence, never inventing a cause where the data is silent.

• Demonstrated evidence of shipping a time your analysis changed what a business did, and you implemented at least part of it yourself. Insight without execution is exactly the wrong fit for this seat.

• You're AI-native and have built something with it: a side project, agent, scraper, or internal tool. You use AI tools daily as a workflow multiplier (briefs, clustering, audit summarisation, scraping) and still own the final, validated output. You build workflows, not just prompts and we take portfolio over claims.

• Experiment design and statistical rigour: you design a test, run it, and know when a result is noise.

• Systems thinking: you improve the tactic and improve the system colonbehind it, turning a one-off finding into a repeatable process.

• Commercial curiosity: you get to "so what?" unprompted, asking about the client's business model and KPIs, and land every finding as a business consequence revenue, pipeline, or cost not just a metric movement.

• Strong written and verbal English, with the ability to convey findings cleanly to non-technical stakeholders. Reports and QA rationale go straight to clients; the analysis only counts once a client understands it and acts on it.

Nice-to-haves (not required)

• Experience with SEO/analytics tooling (GSC, GA4, Ahrefs, a crawler such as Screaming Frog). If you don’t have it, we’ll ramp you this is teachable.

• Client-facing experience, and commercial awareness.

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Familiarity with B2B SaaS.

Culture fit

You should ideally believe:

• Search has fundamentally changed. Google and backlinks still matter but are no longer the full picture — AI search is a new frontier and you’re excited about the shift.

• AI is a force multiplier, not a crutch. You’re actively using AI daily and building workflows, not just typing into a chat box.

• Speed and quality are both achievable. Everyone here operates at a fast pace, is reliable and takes extreme ownership. There are no passengers at Discovered Labs.

You’ll thrive here if: you get energy from excellent work that makes clients successful, you see gaps in processes and fix