Data Scientist (Mid-level)

Hybrid: 3 days in the office / 2 days remote

Location: London

About RAPP

We are RAPP – world leaders in activating growth with precision and empathy at scale. As a global, next-generation precision marketing agency we leverage data, creativity, technology, and empathy to foster client growth. We champion individuality in the marketing solutions we create, and in our workplace. We fight for solutions that adapt to the individual’s needs, beliefs, behaviours, and aspirations. We foster an inclusive workplace that emphasizes personal well-being.

Role

Are you a data scientist eager to broaden your impact across the full stack of data science? Do you enjoy fast-paced environments, wearing multiple hats, and turning ideas into production-ready solutions? At RAPP, we’re looking for a Data Scientist with a growth mindset, a generalist toolkit, and an appetite to grow within a world-class marketing agency. You’ll work alongside senior data scientists, engineers, strategists, and creatives to design, build, and deploy models that make a real difference for global clients like Ralph Lauren, KFC, and Mercedes. This role is ideal for someone who’s ready to grow quickly and thrives in a collaborative, high-velocity setting. You’ll be part of a world-class team led by George Cushen (https://www.linkedin.com/in/cushen/ ), with deep experience delivering high-impact AI solutions across marketing and customer experience.

What You’ll Do

• Model & Build: Support the design and deployment of pragmatic machine learning solutions — from feature engineering in SQL to model development in Python, and deploying in production environments like AWS.
• Explore & Prototype: Help bring new ideas to life by quickly prototyping new models and frameworks that solve business problems or spark client interest.
• Own & Iterate: Take ownership of smaller workstreams within larger projects, with opportunities to grow into leading entire projects.
• Solve Across the Stack: You’ll work end-to-end — writing clean, testable code, tuning models, working with APIs, and understanding data pipelines and infrastructure.
• Communicate Simply: Share findings and rationale in a clear, concise way, tailored to technical and non-technical audiences.
• Learn Fast, Move Fast: Bring energy, curiosity, and clarity of thought to everything you do. Pace and impact matter here.

What You’ll Bring

Must-Have:

• A degree in a STEM discipline (Computer Science, Maths, Engineering, etc.) or equivalent practical experience.
• 2–4 years of experience delivering DS/ML solutions in production environments — ideally in settings where you've had to wear multiple hats (e.g., startups, small teams).
• Fluency in Python and SQL; experience building and deploying models end-to-end, from feature engineering to performance validation.
• Comfort with cloud tools (AWS preferred), Git, and CI/CD pipelines.
• Ability to work independently and juggle priorities without getting stuck in analysis paralysis.
• Concise communication and documentation skills, especially under time pressure.

Nice-to-Have:

• Experience with marketing data or customer-level modelling (e.g., uplift, attribution, causal AI, graph AI, campaign optimization, spend optimization).
• Exposure to MLOps tools like MLflow, FastAPI, Airflow, or similar.
• Experience with experimentation and validation frameworks (e.g., A/B testing).
• Startup or freelance experience that required pace, clarity, and autonomy.

Why This Role is Different

Unlike many mid-level roles, this isn’t a one-track position. You won’t just tune models or clean data — you’ll do it all, with support from senior team members, but autonomy to explore, experiment, and deliver. This is the perfect next step for a generalist with technical foundations and the hunger to grow into a senior leader in a multi-disciplinary environment.