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SE RankingLead Applied Scientist - AI Search & Brand Intelligence
SE Ranking, a company whose products cover SEO and digital marketing, is hiring a Lead Applied Scientist to study how generative assistants and AI search tools…
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About This Role
SE Ranking, a company whose products cover SEO and digital marketing, is hiring a Lead Applied Scientist to study how generative assistants and AI search tools recommend and describe brands. The job also covers building the measurement layer that shows what AI visibility is worth to a business. The company calls this a founding-level role in a new discipline. The job is mainly hands-on technical work with some leadership. About…
Job Description
SE Ranking, a company whose products cover SEO and digital marketing, is hiring a Lead Applied Scientist to study how generative assistants and AI search tools recommend and describe brands. The job also covers building the measurement layer that shows what AI visibility is worth to a business. The company calls this a founding-level role in a new discipline.
The job is mainly hands-on technical work with some leadership. About 45 percent of time goes to research and modeling that the person runs personally, about 45 percent to technical and project leadership such as setting priorities for hypotheses and reviewing methods, and about 10 percent to managing a team of one or two people, including technical direction, one-to-one meetings and help with hiring.
In the first 90 days the person would build a repeatable experiment pipeline with the SEO and GEO specialists and selected engineers, run the first three to four experiments, define candidate product features with the product team, design sampling of AI responses across regions and topics, and create an evaluation framework that keeps results comparable over time.
Ongoing work includes running large batches of LLM calls, open-weight models included, as scoring and extraction tools; setting validation standards with leakage checks and out-of-time testing; building gradient boosting and learning-to-rank models for brand visibility and AI search traffic; running queries in ClickHouse or another columnar store against very large search, backlink, content and audit datasets; and mentoring the machine learning engineers on the team.
The posting asks for five to six or more years in data or applied science, a history of turning unclear research questions into shipped decisions, strong grounding in ranking methods, current LLM experience including judging model outputs or evaluating agents, and English at B2 level or above.
SE Ranking offers direct access to its search, backlink, content and audit datasets, Planable social media data, an LLM budget for sampling AI responses, analytics engineering support and room to publish. Success is measured by how quickly hypotheses become documented insights and how many reach the product roadmap or GEO best practice.
Responsibilities
- Stand up a reusable experiment flow running from hypothesis through data scope, design, execution, insight and decision
- Run the first three to four experiments and help define candidate AI-visibility features with Product
- Design sampling plans that cover topics, intents and regions, handling variance and bias explicitly
- Create a scoring framework for LLM outputs and prompts so results stay comparable over time
- Use LLMs in batch pipelines for entity extraction, sentiment, prompt evaluation and agent testing
- Own validation standards, including leakage detection and out-of-time testing, with metrics tied to business outcomes
- Build gradient boosting and learning-to-rank models, plus embedding-based clustering of how AI systems position brands
- Analyze very large columnar datasets directly
- Mentor the ML engineers, review their methods, delegate work and help with recruiting
- Turn findings into product features, research, talks and GEO best practice
Requirements
- Five to six or more years in data or applied science, plus a history of moving ambiguous problems to decisions
- Strong grounding in ranking methods such as learning to rank, NDCG, precision at K and LambdaMART
- Current GenAI and LLM skills: prompting and evaluating both hosted APIs and open-weight models
- Experience judging model outputs with LLMs or evaluating agents
- Rigorous model validation, including leakage checks and out-of-time testing
- Able to direct one or two people day to day: technical direction, one-to-ones and unblocking
- English at B2 level or above, enough to explain results to marketers and engineers in one meeting
Qualifications
- Gradient boosting with LightGBM or CatBoost
- Embeddings and representation learning using sentence-transformers or faiss
- Large-scale analysis in ClickHouse without relying on a separate data engineer
- Interest in SEO and search, plus familiarity with AI visibility concepts such as share of prompt and share of model
- Experience tracking experiments with MLflow or a comparable tool
- Publications or talks at academic or industry venues
Core Skills
Benefits
- Direct access to SE Ranking and Planable datasets
- Dedicated data and analytics engineering support
- An LLM API budget reserved for sampling AI responses systematically
- Room to share work at events and in writing, including GEO best practice
- Freedom to start experiments on your own judgment
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this job remote?
The listing is marked remote, and the posting does not add further location details.
How is the time split?
The posting describes roughly 45 percent hands-on research and modeling, 45 percent technical and project leadership, and 10 percent people management of a team of one or two.
What experience is required?
Five to six or more years in data or applied science, plus strong ranking and information retrieval skills and current LLM experience.
Is a salary stated?
The posting does not give a salary figure.
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