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- Tasks: Lead the development of forecasting models for ad revenue and website traffic.
- Company: Join a major media organisation focused on digital performance.
- Benefits: Enjoy flexible working arrangements and the chance to work with cutting-edge technology.
- Why this job: Make a real impact by predicting trends and collaborating with diverse teams.
- Qualifications: Expertise in time series forecasting, strong Python and SQL skills required.
- Other info: Opportunity to work in a cloud environment with modern deployment tools.
The predicted salary is between 43200 - 72000 £ per year.
We're hiring a contract Data Scientist to lead the development of forecasting models for a major media organisation. The project is fully scoped and delivery-focused; you'll be responsible for building daily, weekly, and monthly time series models to predict ad revenue, website traffic, and overall digital performance.
Key Responsibilities- Build and deploy time series forecasting models to predict traffic, ad performance, and revenue
- Work across multiple temporal resolutions (daily, weekly, monthly)
- Collaborate with stakeholders in product, marketing, and commercial teams to define inputs and targets
- Work with structured web analytics and revenue data in a cloud environment
- Ensure models are robust, explainable, and production-ready
- Python (NumPy, Pandas, scikit-learn)
- SQL
- GCP (BigQuery, Cloud Functions)
- Deployment: Docker, Kubernetes, Airflow
- Git, CI/CD pipelines
- Tableau (optional)
- Proven expertise in time series forecasting at multiple projects
- Strong Python and SQL skills
- Experience deploying models into production in a cloud environment (GCP preferred)
- Ability to work independently and manage delivery from end to end
- Strong communication skills for cross-team collaboration
Contact Detail:
Job Traffic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Timeseries Data Scientist (Contract)
✨Tip Number 1
Familiarise yourself with the specific time series forecasting techniques that are relevant to the media industry. Understanding how to apply models like ARIMA, Prophet, or LSTM in a practical context will give you an edge during discussions with our team.
✨Tip Number 2
Brush up on your Python and SQL skills, particularly focusing on libraries like NumPy, Pandas, and scikit-learn. Being able to demonstrate your proficiency in these tools during technical conversations will show us you're ready for the role.
✨Tip Number 3
Gain hands-on experience with cloud environments, especially GCP. If you can showcase any projects where you've deployed models using BigQuery or Cloud Functions, it will highlight your ability to work in our tech environment.
✨Tip Number 4
Prepare to discuss your previous experiences in cross-team collaboration. We value strong communication skills, so think of examples where you've successfully worked with product, marketing, or commercial teams to achieve project goals.
We think you need these skills to ace Timeseries Data Scientist (Contract)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with time series forecasting, Python, and SQL. Include specific projects where you've built and deployed models, especially in a cloud environment.
Craft a Compelling Cover Letter: In your cover letter, emphasise your ability to work independently and manage projects from start to finish. Mention your strong communication skills and how you've collaborated with different teams in the past.
Showcase Relevant Projects: If you have examples of previous work related to forecasting models or web analytics, include them in your application. This could be links to GitHub repositories or case studies that demonstrate your expertise.
Highlight Technical Skills: Clearly list your technical skills relevant to the role, such as proficiency in Python libraries (NumPy, Pandas, scikit-learn), SQL, and any experience with GCP tools. Mention any familiarity with deployment technologies like Docker and Kubernetes.
How to prepare for a job interview at Job Traffic
✨Showcase Your Time Series Expertise
Be prepared to discuss your previous projects involving time series forecasting. Highlight specific models you've built, the challenges you faced, and how you overcame them. This will demonstrate your hands-on experience and understanding of the subject.
✨Familiarise Yourself with the Tech Stack
Make sure you have a solid grasp of the tools mentioned in the job description, especially Python libraries like NumPy and Pandas, as well as SQL. If you have experience with GCP, Docker, or Kubernetes, be ready to discuss how you've used these technologies in past projects.
✨Prepare for Collaboration Questions
Since the role involves working with various teams, think of examples where you've successfully collaborated with stakeholders. Be ready to explain how you defined inputs and targets in previous projects and how you ensured everyone was aligned on goals.
✨Communicate Clearly and Effectively
Strong communication skills are essential for this role. Practice explaining complex technical concepts in simple terms, as you may need to present your findings to non-technical stakeholders. This will show that you can make your work accessible and understandable to all.
