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For Companies At a Glance
- Tasks: Develop and enhance forecasting models using cutting-edge Machine Learning techniques.
- Company: Join an innovative Tech company transforming hospitality and transportation with data-driven insights.
- Benefits: Enjoy remote work flexibility and a competitive salary of up to £70,000.
- Why this job: Be at the forefront of AI and ML, making impactful decisions in a dynamic environment.
- Qualifications: PhD preferred; strong experience in forecasting models and production-level Python skills required.
- Other info: This role does not offer sponsorship.
The predicted salary is between 42000 - 84000 £ per year.
Senior Data Scientist
Remote (quarterly travel to London)
Up to £75,000
We are working with a well-established media group with a diverse portfolio of household brands spanning publishing, entertainment, and digital products. The business is investing heavily in analytics and data, with the aim of transforming how customers experience their products and services.
You\’ll be the first Data Scientist to join a team currently made up of analysts and Data Engineers, reporting directly into the CDO. This is a role with real scope for impact – delivering high-value projects across multiple brands, helping prove the case for data science, and laying the groundwork for a team that will grow around you.
What you\’ll be doing
- Apply data science to varied datasets across multiple brands and business areas.
- Lead on projects such as:
- Content personalisation – enriching and tagging large content libraries with AI to deliver smarter recommendations.
- Customer segmentation & clustering – building behavioural insights to improve targeting and retention.
- CRM optimisation – shaping push notifications, email, and advertising strategies with data-driven solutions.
- Research engine development – partnering with external agencies to enhance access to customer insights.
- Identify where data science can make the biggest difference and communicate that impact clearly to stakeholders.
- Collaborate with Analysts, a Data Engineer, and Project Managers, while working alongside established Data Scientists in another brand within the group.
About you
- A couple of years\’ data science experience (open to ambitious candidates ready to step up).
- Strong communicator – able to explain the \”why\” behind the work, not just the \”how\”.
- Ideally from a background where you\’ve had to be hands-on and proactive, rather than just one cog in a large machine.
- Experience with subscriptions, publishing, or digital products is a plus, but not essential.
- Education is less important than curiosity, drive, and the ability to deliver.
The process
- Introductory interview
- Take-home exercise (2–3 hours)
- Presentation
- Final culture fit conversations with the wider team
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and forecasting techniques. Being able to discuss recent advancements or case studies during your interview can demonstrate your passion and expertise in the field.
✨Tip Number 2
Prepare to showcase your hands-on experience with Python and relevant libraries like Pandas and Scikit-learn. Consider working on a personal project or contributing to open-source projects that highlight your skills in building forecasting models.
✨Tip Number 3
Brush up on your communication skills, especially when it comes to explaining complex technical concepts to non-technical stakeholders. Practising how to convey your ideas clearly can set you apart in interviews.
✨Tip Number 4
Network with professionals in the data science community, particularly those who work in forecasting or related fields. Engaging in discussions or attending meetups can provide valuable insights and potentially lead to referrals for the position.
We think you need these skills to ace Senior Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with forecasting models and machine learning techniques. Use specific examples that demonstrate your skills in Python programming and time series analysis.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your background aligns with their goals, particularly in developing forecasting models and working with cross-functional teams.
Showcase Relevant Projects: If you have worked on relevant projects, include them in your application. Describe your role, the technologies used, and the impact of your work on the project's success, especially in relation to AI and ML.
Highlight Continuous Learning: Mention any recent courses, certifications, or workshops you've completed related to AI, ML, or data science. This shows your commitment to staying current with state-of-the-art techniques, which is crucial for this role.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and the specific libraries mentioned in the job description, such as Pandas and Scikit-learn. Bring examples of forecasting models you've built and be ready to explain your approach and the results.
✨Demonstrate Your Understanding of Time Series Analysis
Since this role heavily involves time series analysis, brush up on key methodologies and be ready to discuss how you've applied them in past projects. Highlight any innovative techniques you've used to improve model performance.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would approach designing a forecasting model for a specific business objective and be ready to articulate your thought process.
✨Engage with Stakeholders
This role requires collaboration with both technical and non-technical stakeholders. Prepare to discuss how you've effectively communicated complex data concepts to diverse audiences in the past, showcasing your ability to bridge the gap between data science and business needs.
