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-nativ