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Himalayas · Partner listing

Junior Data Scientist

Seedtag

⌖ Office location not confirmedSalary not providedNot specifiedNot specified · Full-time

Source: Himalayas ↗

Location & working arrangement

The advert contains unclear or conditional office / remote requirements. Confirm the working arrangement and office location with the employer.

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

Provider location / applicant eligibility: Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom

About the role

We’re looking for a Junior Data Scientist to join our tech team and help build the future of neuro-contextual advertising at global scale. Who We Are At Seedtag, our mission is to transform advertising by proving that effectiveness and user privacy can truly coexist. As the leading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel and what they intend to do next. Our proprietary AI, Liz, enables brands to connect with audiences across the open web and Connected TV without cookies or user tracking. Founded in 2014 by two ex-Googlers, Seedtag has grown to 500+ Seedtaggers in 17 countries, backed by €250M in funding, and operates today as a global ad-tech leader. If you enjoy solving complex engineering challenges and building AI-driven systems at scale, you’ll feel right at home here. Your Challenge 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. Our Core Values Outcome over OutputWe measure success by impact and value, not by volume of features or lines of code. Failure Is Allowed, Learning Is a MustExperimentation is key to innovation. We test early, iterate often, and learn fast. We Are All ScoutsWe take ownership and leave things better than we found them. We Are Data-DrivenData informs our decisions and helps us continuously improve our systems and results. Tech Stack 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. What You’ll Need to Succeed 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. Why Join Seedtag? 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. Additional Perks Home office setup budget up to €1,000 Paid trips to our HQ in Madrid MacBook Pro M3 Ready to Join the Seedtag Adventure? At Seedtag, we create an environment where everyone can thrive. If you need accommodations during the hiring process, let us know and we’ll ensure a positive experience. Send us your CV and let’s build the future of neuro-contextual advertising together. Originally posted on Himalayas

Skills mentioned in the listing

Special educational needsPythonSQLGitGoogle CloudData scienceMachine learningNatural language processingPandasNumPyData engineeringUnit testing

Recognised skills are extracted from the supplied description using canonical names and aliases. They are mentions, not verified essential requirements. Check the source advert for the employer’s full requirements and location restrictions.

Why these skills appear

Special educational needs · mentioned
uring the hiring process, let us know and we’ll ensure a positive experience. Send us your CV and let’s build the future of neuro-contextual advertising together. Originally posted on Himalayas

Python · mentioned
o 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, cla

SQL · mentioned
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, classificat

Git · requirement-context
laude Agent SDKs. Cloud & Data Platforms: GCP, BigQuery. Development Tools: Git, unit testing, code review. What You’ll Need to Succeed 0–2 years of experience in Data Science, Analytics, or Machine Learning, incl

Google Cloud · requirement-context
Graph, PydanticAI, MCP, OpenAI and Claude Agent SDKs. Cloud & Data Platforms: GCP, BigQuery. Development Tools: Git, unit testing, code review. What You’ll Need to Succeed 0–2 years of experience in Data Science,

Data science · requirement-context
ing, code review. What You’ll Need to Succeed 0–2 years of experience in Data Science, Analytics, or Machine Learning, including internships, research, or personal projects. Degree in Computer Science, Mathematics,

Machine learning · mentioned
L, 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 C

Natural language processing · mentioned
ading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel an

Pandas · mentioned
ble 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,

NumPy · mentioned
ghts 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, clusteri

Data engineering · mentioned
k 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 Framew

Unit testing · requirement-context
Agent SDKs. Cloud & Data Platforms: GCP, BigQuery. Development Tools: Git, unit testing, code review. What You’ll Need to Succeed 0–2 years of experience in Data Science, Analytics, or Machine Learning, including