Semrush

Senior Data Scientist

Semrush

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇨🇾 Cyprus

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Job Level

Senior

Tech Stack

AirflowBigQueryDockerETLGoogle Cloud PlatformKubernetesPythonSQL

About the role

  • Turn ideas into reality using code, algorithms, and ML techniques for Semrush’s platform
  • Develop and implement ML/NLP models for AI products (AIO, AI Summarization)
  • Integrate LLMs into workflows and build LLM-powered features such as SEO analysis and AI-driven content suggestions
  • Build metrics/features like keyword search volumes, personalized keyword difficulty, search intent, brand performance, and authority score
  • Develop and maintain data pipelines and ETL processes (Airflow, CloudSQL, BigQuery, ClickHouse)
  • Deploy, monitor and maintain production LLM-based solutions using GCP/Vertex AI, Docker, Kubernetes
  • Collaborate with a cross-functional team of Senior Data Scientists, Backend Developer, MLOps Engineer, and Tech Lead

Requirements

  • 3+ years of experience as a DS or MLE
  • Knowledge of Natural Language Processing (NLP) techniques (TF-IDF, BERT, FastText)
  • Strong understanding of machine learning algorithms, probability theory, statistics, and linear algebra
  • High proficiency in Python and the ML stack
  • Hands-on experience with LLMs
  • Knowledge of SQL
  • Practical knowledge of RAG and building AI agents (plus)
  • Experience deploying and maintaining LLM-based solutions in production
  • Experience with GCP and Vertex AI
  • Experience developing ETL processes with Airflow
Benefits
  • Flexible working hours
  • Unlimited PTO
  • Flexi Benefit for your hobby
  • Employee Support Program
  • Loss of family member financial aid
  • Employee Resource Groups
  • Meals, snacks, and drinks at the office
  • Corporate events
  • Teambuilding
  • Training, courses, conferences

ATS Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
machine learningnatural language processingPythonSQLETL processesdata pipelinesLLMsstatisticsprobability theorylinear algebra
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