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For Companies At a Glance
- Tasks: Join a dynamic team to build and deploy innovative ML solutions.
- Company: Be part of a global leader in marketing and customer experience.
- Benefits: Enjoy a hybrid work model and competitive salary with growth opportunities.
- Why this job: Make a real impact while exploring diverse data science challenges.
- Qualifications: Degree in STEM or equivalent experience; 1-4 years in ML solutions.
- Other info: Ideal for those eager to learn and lead in a fast-paced environment.
The predicted salary is between 42000 - 63000 £ per year.
A hybrid opportunity to join a high-performing data team at a global marketing and customer experience company. You’ll work across a variety of data science challenges — from prototyping ideas to deploying full-scale ML solutions — in a fast-paced, collaborative environment.
This role is ideal for someone with strong technical foundations and a generalist mindset, looking to grow quickly and make a tangible impact across major client projects.
As a Data Scientist, you will:
- Build and deploy machine learning solutions end-to-end, from SQL-based feature engineering to model development in Python and cloud deployment (AWS).
- Prototype and test new ideas that solve real business problems or spark client innovation.
- Take ownership of workstreams within larger projects, with opportunities to lead as you grow.
- Work across the stack — from APIs and infrastructure to model tuning and validation.
- Communicate clearly with both technical and non-technical audiences.
- Bring curiosity, pace, and clarity to everything you do.
Your Skills and Experience
Must-Have:
- Degree in a STEM field or equivalent hands-on experience.
- 1–4 years’ experience delivering ML solutions in production, ideally in fast-moving teams or startups.
- Strong Python and SQL skills; experience building and deploying models end-to-end.
- Familiarity with AWS (or similar), Git, and CI/CD pipelines.
- Ability to work independently and manage priorities in a high-velocity environment.
- Excellent communication and documentation under pressure.
Nice-to-Have:
- Experience with marketing data or customer-level models (e.g. uplift, attribution, causal inference, campaign optimization).
- Familiarity with MLOps tools (e.g. MLflow, FastAPI, Airflow).
- Exposure to A/B testing and experimentation frameworks.
Why This Role is Different
This isn’t a narrow data science role — you won’t just tune models or clean data. You’ll do it all, with support where needed and freedom to explore. It’s the perfect step for someone who wants to move fast, own their growth, and help shape impactful AI solutions in a cross-disciplinary setting.
How to Apply
Interested? Apply via the link on this page with your CV.
Data Scientist - Marketing employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Marketing
✨Tip Number 1
Familiarise yourself with the specific machine learning solutions that are relevant to marketing. Research case studies or projects where data science has significantly impacted marketing strategies, as this will help you speak confidently about your understanding of the role during interviews.
✨Tip Number 2
Brush up on your Python and SQL skills by working on personal projects or contributing to open-source initiatives. This hands-on experience will not only enhance your technical abilities but also provide you with concrete examples to discuss in interviews.
✨Tip Number 3
Network with professionals in the data science and marketing fields. Attend industry meetups or webinars to connect with others who work in similar roles. This can lead to valuable insights and potentially even referrals for the position.
✨Tip Number 4
Prepare to demonstrate your ability to communicate complex data concepts to non-technical audiences. Practice explaining your past projects in simple terms, as this skill is crucial for the role and will set you apart from other candidates.
We think you need these skills to ace Data Scientist - Marketing
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly focusing on your skills in Python, SQL, and any machine learning projects you've worked on. Emphasise your ability to communicate with both technical and non-technical audiences.
Showcase Your Projects: Include specific examples of machine learning solutions you have built and deployed. Detail your role in these projects, the technologies used (like AWS), and the impact they had on the business or clients.
Craft a Compelling Cover Letter: Write a cover letter that reflects your passion for data science and marketing. Discuss how your background aligns with the company's goals and how you can contribute to their data team. Mention your curiosity and desire to innovate.
Prepare for Technical Questions: Be ready to discuss your technical skills in detail during the interview process. Brush up on your knowledge of machine learning concepts, SQL queries, and Python programming. Prepare to explain your thought process in solving data-related problems.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and SQL in detail. Bring examples of machine learning solutions you've built and deployed, and be ready to explain the end-to-end process you followed.
✨Demonstrate Your Problem-Solving Ability
Think of specific business problems you've solved using data science. Be ready to share how you approached these challenges, the methods you used, and the impact your solutions had on the business.
✨Communicate Clearly
Since you'll need to communicate with both technical and non-technical audiences, practice explaining complex concepts in simple terms. This will show your ability to bridge the gap between different stakeholders.
✨Express Your Curiosity and Growth Mindset
This role values curiosity and a desire to learn. Share examples of how you've pursued new knowledge or skills in the past, and express your enthusiasm for exploring innovative solutions in data science.
