Junior Data Scientist

• 0-2 years of experience in Data Science, Analytics, or Machine Learning, including internships, research, or personal projects.

• Degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field, or equivalent practical experience.

• Strong Python and SQL skills, with a solid understanding of statistics, machine learning, and model evaluation.

• Hands-on experience building LLM-based agents or agentic workflows, including tool calling, structured outputs, or multi-step processes.

• Good software engineering practices, including Git, testing, and clean, reproducible code.

• Strong analytical thinking and data storytelling skills, with the ability to communicate insights to non-technical stakeholders.

• Curious, proactive, and eager to learn, with professional working proficiency in English.

• A key moment of growth with real ownership and global impact.

• Flexible work model with 100% remote or hybrid options.
  (Remote contracts available in Spain, Italy, UK, Belgium, Netherlands, France, and Germany.)

• Continuous learning through a learning platform and optional language classes.

• A supportive, trust-based culture that values well-being.

• Team activities, offsites, and opportunities to connect beyond work.

• Home office setup budget up to €1,000

• Paid trips to our HQ in Madrid

• MacBook Pro M3

We're looking for a [Job Title] to join our tech team and help build the future of neuro-contextual advertising at global scale.

As our Junior Data Scientist, you will join our Insights team to transform contextual, audience, and campaign data into actionable insights for clients and commercial teams, leveraging Liz, Seedtag's AI.

Your main responsibilities will include:

• Data Pipelines & Analytics: Build and maintain pipelines to analyse campaign performance, audience behaviour, and contextual data across the open web and CTV.

• Analytical Models: Develop models for audience profiling, content analysis, campaign comparisons, and pre-campaign planning.

• Data Products: Turn analyses into scalable data products using tools such as dbt, Trino, and Airflow, ensuring data quality and reliability.

• AI Agents & Automation: Prototype LLM-based agents and workflows using techniques such as embeddings, classification, clustering, and summarisation.

• Experimentation & Evaluation: Design and evaluate experiments to ensure automated insights are accurate, reliable, and actionable.

• Data Quality & Troubleshooting: Investigate data anomalies, identify root causes, and improve pipeline reliability.

• Data Storytelling: Communicate findings through clear visualisations and actionable recommendations for Product, Sales, and clients.

Outcome over Output
We measure success by impact and value, not by volume of features or lines of code.

Failure Is Allowed, Learning Is a Must
Experimentation is key to innovation. We test early, iterate often, and learn fast.

We Are All Scouts
We take ownership and leave things better than we found them.

We Are Data-Driven
Data informs our decisions and helps us continuously improve our systems and results.

You'll work with modern data and AI technologies to build scalable insights products.

Our stack includes:

• Languages & Libraries: Python, SQL, pandas, NumPy, scikit-learn.

• Data Engineering: dbt, Trino, Airflow.

• AI & Machine Learning: LLMs, AI agents, embeddings, classification, clustering, NLP.

• Agent Frameworks: LangGraph, PydanticAI, MCP, OpenAI and Claude Agent SDKs.

• Cloud & Data Platforms: GCP, BigQuery.

• Development Tools: Git, unit testing, code review.