AI Data Scientist

About

First project

The role

• Develop personalization, recommendation, ranking, segmentation, and next-best-action solutions using behavioural, content, and contextual data.
• Design and analyse A/B tests, including hypotheses, success metrics, guardrails, sample-size requirements, and decision criteria.
• Build machine learning models and analytical solutions that address measurable product and business outcomes.
• Develop generative AI applications, including retrieval-augmented generation, semantic search, tool-calling workflows, and AI agents.
• Build the data pipelines, APIs, and services required to integrate AI capabilities into production systems.
• Evaluate solutions for model quality, business impact, reliability, latency, security, and cost.
• Implement production safeguards such as monitoring, validation, fallbacks, access controls, and human approval steps.
• Work with client product, engineering, data, marketing, and editorial teams to translate business needs into technical solutions.
• Communicate assumptions, results, trade-offs, and recommendations clearly to technical and non-technical stakeholders.
• Contribute reusable components, delivery practices, and technical standards across engagements.

You have

• Typically 3+ years of professional experience in data science, machine learning engineering, AI engineering, or a related applied role.
• Strong proficiency in Python and SQL, with experience writing clean, tested, maintainable production code.
• Experience building and deploying machine learning, data, or AI applications beyond the proof-of-concept stage.
• Strong foundations in statistics, machine learning, model evaluation, and experimental design.
• Experience working with large, complex, or behavioural datasets using batch or distributed processing.
• Familiarity with software engineering practice: Git, automated testing, APIs, code review, documentation, and CI/CD.
• Experience with at least two of the following: personalization, recommendation systems, ranking or search; product experimentation, causal measurement or behavioural analytics; generative AI, retrieval-augmented generation or agentic workflows; data engineering, cloud infrastructure or production MLOps.
• Ability to evaluate technical approaches on business value, complexity, scalability, reliability, and cost.
• Strong communication skills and confidence working directly with clients and multidisciplinary teams.
• Comfort operating in ambiguous environments, learning unfamiliar systems, and moving between strategy and implementation.
• A degree in computer science, engineering, statistics, mathematics, or a related discipline, or equivalent practical experience.

Bonus

• Experience in media, publishing, streaming, advertising, e-commerce, or subscription businesses.
• Experience building content recommendations, personalized feeds, audience segments, or marketing decisioning systems.
• Knowledge of contextual bandits, uplift modelling, causal inference, or reinforcement learning.
• Experience evaluating and monitoring LLM applications, covering hallucination, retrieval quality, prompt injection, latency, and cost.
• Familiarity with product analytics, customer-data, or marketing platforms such as Amplitude, Mixpanel, Segment, Braze, or Adobe Experience Platform.
• Experience processing unstructured or multimodal data such as documents, images, audio, transcripts, or video.
• Experience in a consultancy, agency, startup, or other client-facing delivery environment.

Environment

First months

• You understand the client's users, content, data, and business objectives.
• You have delivered an experiment, personalization capability, or AI workflow that demonstrates measurable value.
• You have established reliable approaches to evaluation, deployment, monitoring, and iteration.
• You have built trusted relationships with client product, engineering, data, and business stakeholders.
• You are helping turn successful prototypes into scalable production capabilities.

Perks

• Remote-first anywhere in Spain. The team gets together in person a few times a year, and you work wherever you work best the rest of the time.
• 22 days of paid time off, plus public holidays.
• Ticket Restaurant meal allowance and private health insurance.
• A learning budget for conferences, courses, and books.
• Access to the latest AI agents and tooling to help you do the work.
• A Mac, and the rest of the setup you need to work comfortably.

Why join

• Work on high-impact AI products used by large and engaged audiences.
• Get exposure to personalization, experimentation, machine learning, and generative AI in the same role.
• Work directly with clients and influence both product direction and technical architecture.
• Help shape the reusable tools, standards, and delivery practices of a growing AI consultancy.

Process

1. 01

   Intro call

   Thirty minutes with a founder. What you have built, what you are looking for, and what the work here actually involves.

2. 02

   Take-home assessment

   A practical exercise close to the work we do, completed in your own time. We design it to take about three hours, and we will not ask for more than that.

3. 03

   Technical deep dive

   We go through your solution together: the decisions you made, the trade-offs you weighed, and what you would change with more time.

4. 04

   Feedback or offer

   We come back to you either way, with feedback you can use.

The whole process usually takes two to three weeks from the intro call, and we work around your schedule.