Lead Applied Scientist - AI Search & Brand Intelligence at SE Ranking

<p>SE Ranking, an all-in-one SEO and digital marketing platform, is looking for a <strong>Lead Applied Scientist </strong>to reverse-engineer how AI search and generative platforms (ChatGPT, Gemini, Perplexity, AI Overviews) recommend and describe brands - and to build the measurement layer that proves what AI visibility is actually worth to a business.</p> <p>This is a founding-level role for a new discipline: AI Search Optimization and Brand Intelligence for Generative AI. This is not classic SEO, and it is not classic data science. You will be the technical owner of the research agenda - from hypothesis to experiment to insight to shipped product capability - and you will work alongside a small team (one ML Engineer today, a second one you will help hire), while staying deeply hands-on yourself.</p> <h2><strong>Why this role is different</strong></h2> <ul> <li>You get proprietary data most researchers can only dream about: SE Ranking's SERP, backlink, content, audit, GA and GSC datasets, plus Planable's social media data - combined with systematically sampled AI-generated responses</li> <li>Your output is not only product features. It's the GEO best practice the industry will end up using - with the opportunity to represent that work at conferences and in company research, if that's something you'd enjoy</li> <li>The discipline is barely a few years old. Nobody is "the expert" yet - the first rigorous answers will come from someone with your access to data</li> </ul> <h2><strong>How the role splits</strong></h2> <p>This is primarily a hands-on technical role with some team leadership on top - you'll stay deeply hands-on in research and modelling, while also directing a small team's day-to-day work: setting technical direction, running 1:1s, unblocking people, and contributing to hiring decisions:</p> <ul> <li>~45% hands-on research and modelling - you design and run experiments yourself</li> <li>~45% technical and project leadership - hypothesis prioritisation, decomposition, methodology review</li> <li>~10% people management - directing a small team's day-to-day (1-2 people): setting technical direction, running 1:1s, unblocking people</li> </ul> <h2><strong>What you'll do - first 90 days</strong></h2> <ul> <li>Partner with our SEO/GEO specialists and selected engineers to set up a repeatable experiment pipeline: hypothesis → data scope → design → run → insight → decision</li> <li>Run the first 3-4 experiments and, with Product, define candidate features that would give our customers a real AI-visibility practice</li> <li>Design sampling methods to systematically collect and analyse AI responses across topics, intents and regions - with explicit handling of variance, bias and representativeness</li> <li>Establish an evaluation framework for LLM outputs, prompts and model behaviour, so results are comparable over time</li> </ul> <h2><strong>What you'll do - ongoing</strong></h2> <ul> <li>Apply LLMs via batch API (including open-weight models) as feature extractors and judges: entity/sentiment/positioning extraction, prompt evaluation on holdout sets, cost-aware batch inference, agentic/tool-using pipelines</li> <li>Own the team's model evaluation &amp; validation standards: leakage detection, temporal/out-of-time validation, and metric definitions tied to business outcomes</li> <li>Develop predictive and ranking models - gradient boosting (LightGBM/CatBoost), learning-to-rank (NDCG, precision@K) - for brand visibility, probability of mention in AI answers, and traffic/impressions in AI search and SEO</li> <li>Build classification, clustering and representation-learning models (embeddings, approximate nearest neighbours) that map how AI systems perceive and position brands</li> <li>Work directly in ClickHouse (or a similar columnar OLAP) on SERP, backlink, content, audit, GA and GSC data at hundreds-of-millions-of-rows scale, on infrastructure shared with the product</li> <li>Source hypotheses from across the company and the market, generate your own from patterns in data, and maintain a prioritised backlog with a clear execution cycle</li> <li>Mentor the ML Engineer(s) you work with: set technical direction, review methodology, delegate meaningfully, and participate in hiring</li> <li>Turn findings into product features, published research, conference talks and our own GEO best practice - working with Product, Engineering, Marketing and Leadership</li> </ul> <h2><strong>What you'll bring&nbsp;</strong></h2> <ul> <li>5-6+ years in Data Science or Applied Science, with a track record of taking ambiguous research problems from hypothesis to a shipped decision</li> <li>Deep, hands-on expertise in ranking &amp; information retrieval (learning to rank, NDCG/precision@K, LambdaRank/LambdaMART) - the core lens for how brands get surfaced by generative AI systems</li> <li>Strong, current GenAI/LLM expertise: using LLMs as an analysis tool - prompting and evaluating both commercial APIs and open-weight models (e.g. Llama, Mistral, Qwen - models we can run on our own infrastructure), LLM-as-judge or agent-evaluation experience, and an understanding of how sampling, context and model behaviour affect what a model "says" about a brand</li> <li>Rigorous model evaluation and validation: leakage detection, temporal/out-of-time validation, and choosing metrics tied to the business outcome</li> <li>Comfortable directing a small team's day-to-day (1-2 people): setting technical direction, running 1:1s, unblocking people - this doesn't require management brilliance, just the willingness and basic ability to do it well</li> <li>Solid English (B2+) - able to explain a complex result to a marketer and a sceptical engineer in the same meeting</li> </ul> <h2><strong>Nice to have</strong></h2> <ul> <li>Gradient boosting (LightGBM/CatBoost) for tabular modelling - we already have models built on this and need someone who can keep improving them</li> <li>Embeddings &amp; representation learning (sentence-transformers, faiss) for semantic features and clustering</li> <li>Comfortable analysing large-scale data directly (e.g. ClickHouse) without depending on a dedicated engineer</li> <li>Genuine interest in the broader SEO/search domain - we don't expect domain expertise on day one, just curiosity and speed</li> <li>Familiarity with AI-search measurement concepts (AI Visibility, Share of Prompt, Share of Model)</li> <li>MLflow (or similar experiment tracking) experience</li> <li>Publications or talks (SIGIR, ECIR, KDD, ACL, EMNLP - or brightonSEO and industry research)</li> </ul> <h2><strong>Mindset</strong></h2> <ul> <li>Growth mindset: you experiment without waiting for approval, adjust based on results, share what you learned, and turn failures into team learning</li> <li>Bias to action: you build the execution cycle first and refine the hypothesis backlog as you go, rather than spending a quarter defining the perfect hypothesis list</li> <li>Product-oriented: you think past the model, toward impact and actionability</li> <li>Critical thinking and the ability to disagree constructively</li> <li>Comfortable in ambiguous, cutting-edge problem spaces - and you escalate fast when blocked on data, rather than waiting</li> </ul> <h2><strong>What we give you to succeed</strong></h2> <ul> <li>Direct, ticket-free access to SE Ranking's datasets - SERP, backlinks, content, audits, GA/GSC - plus Planable's social media data</li> <li>Dedicated data/analytics engineering support, so pipelines are not a solo project</li> <li>A dedicated LLM API budget for systematic response sampling (OpenAI, Google, Anthropic, Perplexity)</li> <li>An opportunity to publish: conference talks, research, and our own best practice</li> <li>Authority to launch experiments without asking for permission first</li> </ul> <h2><strong>How we'll know it's working</strong></h2> <ul> <li>Cycle time from hypothesis to documented insight</li> <li>Actionability of the resulting insights - did they enter the product roadmap or the GEO best practice?</li> <li>Self-sufficiency in gathering and analysing data</li> <li>Number of experiments/ hypotheses run, and how many held up in practice and integrated into the product</li> </ul>