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