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Title: Junior Data Scientist

Level: Junior-Level Engineer, Data & Technology Solutions

Location: Copenhagen

Reporting to: Lead Engineer

About Open Intelligence:

We are the Activation arm of WPP Open Intelligence. Our team builds the foundational data infrastructure and high-performance edge services that power WPP’s position in the market. With approximately one-third of all global media spend flowing through WPP, our platform operates at the core of this massive network—deeply integrated and adopted by the largest supply-side partners in the AdTech industry. Operating at our scale presents unique engineering challenges. Our edge services currently handle over 60,000 classification requests per second, while our contextual classification pipelines process 10,000 requests per second. With operations spanning the US, UK, and ongoing expansion into EMEA and APAC, our infrastructure continuously interacts with up to 98% of the population in our active markets. We are an engineering-led team focused on building robust, scalable, and highly reliable systems.

WHO WE ARE LOOKING FOR

We are looking for a Junior Data Scientist who is curious about turning data into clear, trustworthy insights. You care more about sound statistical thinking, clean analysis, and honest communication of limitations than about chasing any specific tool or model.

You are comfortable learning team standards for exploratory analysis, experimentation, and reproducible work, then delivering well-scoped analyses, metrics, and model-backed features under guidance from senior data scientists and engineers. You help define questions, validate assumptions, and document findings stakeholders can act on. You support building and evaluating models for defined problems, contribute to data quality and feature understanding, and investigate metric or model anomalies with mentorship. You bring strong hands-on skills in Python or R plus solid SQL, a solid grasp of core statistics and machine learning concepts, and familiarity with visualization and storytelling. Experience with notebooks or scripts, Git, and basic validation of analysis code matters; some exposure to a cloud or modern data stack — or strong motivation to learn in production — is enough to start. Openness to AI coding assistants to learn and work more effectively is welcome.

Beyond the technical craft, you communicate clearly with both technical and non-technical audiences, ask questions when stuck, and share learnings in stand-ups. You take ownership of the quality of your analysis and are motivated by continuous learning, experimentation, and growing into production data science with the team.

WHY ARE WE HIRING We are at a critical inflection point in our growth and need to scale our engineering team to tackle an expanding set of complex challenges. As the digital ecosystem evolves, we are actively developing and releasing a new catalog of privacy-preserving data products. These solutions are designed to scale advanced targeting capabilities while remaining uncompromising on our promise to protect user privacy.

To support this product evolution, our infrastructure is undergoing massive global expansion. By the end of this year, we will be fully operational across the rest of the EMEA and APAC regions. Concurrently, we are deepening our integrations to support even more of the largest supply-side partners in the AdTech industry. We are hiring because we need strong engineering talent to help us architect for this next order of magnitude in scale—ensuring our systems remain resilient, performant, and compliant as our global footprint and feature set rapidly expand.

WHAT YOU WILL DO:

· Learn and apply team standards for exploratory analysis, experimentation, and reproducible data science work.

· Partner with product, analysts, and data engineers to deliver well-scoped analyses, metrics, and model-backed features under guidance from senior data scientists and engineers.

· Help define questions, validate assumptions, and document findings so stakeholders can act on clear, trustworthy insights.

· Support building and evaluating models or statistical approaches for defined problems, including basic monitoring and sanity checks after deployment.

· Contribute to data quality checks, feature understanding, and lightweight governance so inputs and outputs stay reliable and interpretable.

· Investigate anomalies in metrics or model behavior, document root causes, and implement fixes or escalations with mentorship.

· Stay curious and proactive: ask questions, share learnings in stand-ups, and explore methods and tools that help the team move faster and smarter.

WHAT YOU WILL NEED

· Clear communication skills and the ability to explain analysis, limitations, and recommendations to technical and non-technical audiences.

· Up to ~2 years of experience in data science, analytics, applied statistics, or a related internship/graduate role.

· Strong hands-on skills in Python or R for analysis, plus solid SQL for querying and transforming data.

· Solid grasp of statistics and core machine learning concepts (e.g. regression, classification, evaluation metrics, train/test splits, overfitting).

· Experience producing clear analysis in notebooks or scripts, with version control (Git) and basic testing or validation of analysis code.

· Comfort working with structured (and ideally some unstructured) datasets; experience cleaning, joining, and aggregating data for analysis.

· Familiarity with visualization and storytelling (e.g. Matplotlib, Seaborn, Plotly, ggplot, or BI tools) to communicate results effectively.

· Some exposure to a cloud or modern data stack (GCP, AWS, or Azure; warehouses, batch pipelines, or feature stores), or strong motivation to learn in production.

· Enthusiasm for data, experimentation, and continuous learning.

· Openness to using AI coding assistants and prompt engineering to learn faster and work more effectively.

NICE TO HAVE

· Familiarity with AdTech- and/or MarTech data, metrics, and terminology.

· Experience with experiment design, A/B testing, or causal thinking at a basic level.

· Exposure to MLOps concepts (model packaging, batch scoring, simple monitoring) or collaboration with data engineering on pipelines.

· Experience contributing in a monorepo or large shared codebase.

· Awareness of data privacy basics and responsible handling of sensitive data.

· Portfolio, Kaggle, coursework, or side projects demonstrating end-to-end analysis or modeling.

If you are ready to be at the forefront of the AdTech industry, shaping its future, and driving success for both our clients, we encourage you to apply and join our team.

We believe the best work happens when we're together, fostering creativity, collaboration, and connection. That's why we’ve adopted a hybrid approach, with teams in the office around four days a week. If you require accommodations or flexibility, please discuss this with the hiring team during the interview process.

WPP is an equal opportunity employer and considers applicants for all positions without discrimination or regard to particular characteristics. We are committed to fostering a culture of respect in which everyone feels they belong

and has the same opportunities to progress in their careers.

#LI-Hybrid

We believe the best work happens when we're together, fostering creativity, collaboration, and connection. That's why we’ve adopted a hybrid approach, with teams in the office around four days a week. If you require accommodations or flexibility, please discuss this with the hiring team during the interview process.

WPP is an equal opportunity employer and considers applicants for all positions without discrimination or regard to particular characteristics. We are committed to fostering a culture of respect in which everyone feels they belong and has the same opportunities to progress in their careers.

Please read our Privacy Notice (https://www.wpp.com/en/careers/wpp-privacy-policy-for-recruitment) for more information on how we process the information you provide.