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- Tasks: Lead data science projects and innovate marketing strategies using machine learning.
- Company: Join a precision marketing agency that combines data, tech, and creativity for client growth.
- Benefits: Enjoy hybrid work, competitive salary, and opportunities for professional development.
- Why this job: Be at the forefront of marketing innovation while mentoring a talented team.
- Qualifications: Strong background in Computer Science or related fields; experience in machine learning is essential.
- Other info: Work with cutting-edge tools and technologies in a collaborative environment.
The predicted salary is between 48000 - 84000 £ per year.
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This range is provided by Harnham. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
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Building Data Science and Machine Learning Teams in the UK | The Talent Driving The Data and AI Revolution
Principal Data Scientist
Up to £85,000
London (Hybrid, 3 days onsite per week)
Company:
Join one of the world’s biggest high street fashion brands, who has been leading the industry for the past 55 years. They are looking for a Principal Data Scientist to lead and scale their data science strategy, helping to unlock insights, optimise performance, and shape the future!
They are on a mission to make a real impact – for our customers and our people. With a strong presence both online and offline, we\’re committed to driving innovation, embracing inclusivity, and creating meaningful experiences through the power of data.
Responsibilities:
- Own the data science strategy and roadmap across the business
- Build, mentor, and scale a high-performing team of data scientists and analysts
- Create scalable solutions to solve real-world challenges like demand forecasting, personalisation, and customer segmentation
- Translate business problems into data science use cases and deliver measurable outcomes
- Use data-driven insights to streamline workflows and improve decision-making in areas like inventory, logistics, and customer journey
- Measure success through improvements in performance metrics like conversion, retention, stock accuracy, and customer satisfaction
Requirements:
- MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields.
- Expert in Python, R, SQL and a range of ML techniques (e.g., random forests, neural nets, reinforcement learning)
- Track record of delivering high-impact AI projects from concept to production
- Strong communication skills – able to translate complex insights into business value
- Passionate about innovation, mentorship, and driving change through data
How to Apply:
Please register your interest by sending your CV to Emily Burgess via the Apply link on this page.
Seniority level
-
Seniority level
Mid-Senior level
Employment type
-
Employment type
Full-time
Job function
-
Job function
Analyst and Engineering
-
Industries
Retail Apparel and Fashion
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Principal Data Scientist employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and AI, especially those relevant to marketing. This will not only help you during interviews but also demonstrate your passion for the field.
✨Tip Number 2
Network with professionals in the data science and marketing sectors. Attend industry meetups or webinars to connect with potential colleagues and learn about their experiences, which can give you insights into the company culture.
✨Tip Number 3
Prepare to discuss specific projects where you've successfully implemented machine learning solutions. Be ready to explain your thought process, the challenges you faced, and how you overcame them.
✨Tip Number 4
Showcase your leadership skills by discussing any mentoring or team management experiences. Highlight how you've guided junior team members and contributed to a collaborative work environment.
We think you need these skills to ace Principal Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning and data science. Emphasise your proficiency in Python, SQL, and any cloud platforms you've worked with, as well as your leadership skills.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about data science and how your background aligns with the company's goals. Mention specific projects or achievements that demonstrate your expertise in predictive modelling and AI solutions.
Showcase Your Technical Skills: Include a section in your application that lists your technical skills, such as experience with Jupyter notebooks, Pandas, PyTorch, and any familiarity with advanced AI techniques like NLP or CausalAI. This will help you stand out to hiring managers.
Prepare for Interviews: If selected for an interview, be ready to discuss your previous projects in detail. Prepare to explain your thought process behind building predictive models and how you've used data to drive business outcomes. Practice articulating complex concepts for both technical and non-technical audiences.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python, SQL, and any relevant tools like Jupyter notebooks or PyTorch. Bring examples of past projects where you applied machine learning techniques, as this will demonstrate your hands-on experience.
✨Demonstrate Leadership Experience
Since the role involves mentoring junior team members, be ready to share specific instances where you've led a team or guided others in data science projects. Highlight your leadership style and how you foster collaboration.
✨Prepare for Case Studies
Expect to tackle case studies or practical problems during the interview. Brush up on predictive modelling and campaign optimisation scenarios, as these are key aspects of the role. Practice articulating your thought process clearly.
✨Communicate Effectively with Stakeholders
You'll need to present insights to both technical and non-technical audiences. Prepare to explain complex concepts in simple terms and think about how you can tailor your communication style to different stakeholders.
