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SE Ranking, an all-in-one SEO and digital marketing platform, is looking for a Lead Applied Scientist 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. 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. Why this role is different 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 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 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 How the role splits 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: ~45% hands-on research and modelling - you design and run experiments yourself ~45% technical and project leadership - hypothesis prioritisation, decomposition, methodology review ~10% people management - directing a small team's day-to-day (1-2 people): setting technical direction, running 1:1s, unblocking people What you'll do - first 90 days Partner with our SEO/GEO specialists and selected engineers to set up a repeatable experiment pipeline: hypothesis → data scope → design → run → insight → decision Run the first 3-4 experiments and, with Product, define candidate features that would give our customers a real AI-visibility practice Design sampling methods to systematically collect and analyse AI responses across topics, intents and regions - with explicit handling of variance, bias and representativeness Establish an evaluation framework for LLM outputs, prompts and model behaviour, so results are comparable over time What you'll do - ongoing 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 Own the team's model evaluation & validation standards: leakage detection, temporal/out-of-time validation, and metric definitions tied to business outcomes 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 Build classification, clustering and representation-learning models (embeddings, approximate nearest neighbours) that map how AI systems perceive and position brands 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 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 Mentor the ML Engineer(s) you work with: set technical direction, review methodology, delegate meaningfully, and participate in hiring Turn findings into product features, published research, conference talks and our own GEO best practice - working with Product, Engineering, Marketing and Leadership What you'll bring 5-6+ years in Data Science or Applied Science, with a track record of taking ambiguous research problems from hypothesis to a shipped decision Deep, hands-on expertise in ranking & information retrieval (learning to rank, NDCG/precision@K, LambdaRank/LambdaMART) - the core lens for how brands get surfaced by generative AI systems 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 Rigorous model evaluation and validation: leakage detection, temporal/out-of-time validation, and choosing metrics tied to the business outcome 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 Solid English (B2+) - able to explain a complex result to a marketer and a sceptical engineer in the same meeting Nice to have Gradient boosting (LightGBM/CatBoost) for tabular modelling - we already have models built on this and need someone who can keep improving them Embeddings & representation learning (sentence-transformers, faiss) for semantic features and clustering Comfortable analysing large-scale data directly (e.g. ClickHouse) without depending on a dedicated engineer Genuine interest in the broader SEO/search domain - we don't expect domain expertise on day one, just curiosity and speed Familiarity with AI-search measurement concepts (AI Visibility, Share of Prompt, Share of Model) MLflow (or similar experiment tracking) experience Publications or talks (SIGIR, ECIR, KDD, ACL, EMNLP - or brightonSEO and industry research) Mindset Growth mindset: you experiment without waiting for approval, adjust based on results, share what you learned, and turn failures into team learning 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 Product-oriented: you think past the model, toward impact and actionability Critical thinking and the ability to disagree constructively Comfortable in ambiguous, cutting-edge problem spaces - and you escalate fast when blocked on data, rather than waiting What we give you to succeed Direct, ticket-free access to SE Ranking's datasets - SERP, backlinks, content, audits, GA/GSC - plus Planable's social media data Dedicated data/analytics engineering support, so pipelines are not a solo project A dedicated LLM API budget for systematic response sampling (OpenAI, Google, Anthropic, Perplexity) An opportunity to publish: conference talks, research, and our own best practice Authority to launch experiments without asking for permission first How we'll know it's working Cycle time from hypothesis to documented insight Actionability of the resulting insights - did they enter the product roadmap or the GEO best practice? Self-sufficiency in gathering and analysing data Number of experiments/ hypotheses run, and how many held up in practice and integrated into the product Originally posted on Himalayas