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- Tasks: Lead the design and deployment of time series models for website and app traffic forecasting.
- Company: Join a dynamic media organisation making waves in the digital landscape.
- Benefits: Enjoy flexible work options, collaborative culture, and opportunities for professional growth.
- Why this job: Make a real impact by collaborating across teams and enhancing data-driven decision-making.
- Qualifications: Experience in time series forecasting and strong programming skills in Python and SQL required.
- Other info: Work with cutting-edge tools and technologies in a fast-paced environment.
The predicted salary is between 36000 - 60000 £ per year.
A Data Scientist to lead the design and deployment of time series models forecasting website and app traffic at daily, weekly, and monthly levels. This is a high-impact, cross-functional role within a media organization, collaborating with teams across revenue, editorial, and product.
Key Responsibilities:
- Lead forecasting projects for monetizable traffic
- Build and deploy models at various time scales (daily, weekly, monthly)
- Audit and improve existing models
- Collaborate with stakeholders across journalism, commercial, and revenue teams
- Ensure models are production-ready using modern pipelines and tools
Required Experience:
- Proven track record in time series forecasting (e.g., ARIMA, Prophet, LSTM)
- Strong experience with large datasets (e.g., traffic, engagement metrics)
- Model deployment experience in production environments
Key Skills:
- Programming: Python (strong), SQL
- Cloud Platforms: GCP (BigQuery), AWS
- Deployment Tools: Airflow, DBT, GitHub
- Collaboration Tools: JIRA, Slack, Notion
- Strong understanding of model evaluation (RMSE, MAPE, cross-validation)
Data Science - Time Series and Forecasting employer: Career Wallet
Contact Detail:
Career Wallet Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science - Time Series and Forecasting
✨Tip Number 1
Familiarise yourself with the specific time series forecasting techniques mentioned in the job description, such as ARIMA, Prophet, and LSTM. Being able to discuss these models in detail during your interview will demonstrate your expertise and enthusiasm for the role.
✨Tip Number 2
Showcase your experience with large datasets by preparing examples of past projects where you successfully handled traffic or engagement metrics. Be ready to explain the challenges you faced and how you overcame them, as this will highlight your problem-solving skills.
✨Tip Number 3
Brush up on your knowledge of cloud platforms like GCP and AWS, especially BigQuery. If you have any hands-on experience with these tools, be sure to mention it, as familiarity with the deployment environment is crucial for this role.
✨Tip Number 4
Prepare to discuss your collaboration experiences with cross-functional teams. Since this role involves working closely with journalism, commercial, and revenue teams, being able to share examples of successful teamwork will set you apart from other candidates.
We think you need these skills to ace Data Science - Time Series and Forecasting
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with time series forecasting and relevant programming skills. Include specific projects where you've used models like ARIMA or LSTM, and mention any large datasets you've worked with.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about data science and how your skills align with the role. Mention your experience in collaborating with cross-functional teams and your understanding of model evaluation metrics.
Showcase Your Technical Skills: Be explicit about your proficiency in Python, SQL, and cloud platforms like GCP and AWS. If you have experience with deployment tools such as Airflow or GitHub, make sure to highlight that as well.
Prepare for Potential Questions: Think about how you would discuss your previous forecasting projects during an interview. Be ready to explain your approach to model auditing and improvement, as well as how you ensure models are production-ready.
How to prepare for a job interview at Career Wallet
✨Showcase Your Technical Skills
Be prepared to discuss your experience with time series forecasting techniques like ARIMA, Prophet, and LSTM. Bring examples of past projects where you successfully implemented these models, and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Collaboration Experience
Since this role involves working with various teams, highlight your experience in cross-functional collaboration. Share specific examples of how you've worked with stakeholders from different departments, such as journalism or commercial teams, to achieve common goals.
✨Discuss Model Deployment
Talk about your experience with deploying models in production environments. Be specific about the tools you've used, such as Airflow or DBT, and explain how you ensure that your models are production-ready and maintainable over time.
✨Prepare for Technical Questions
Expect technical questions related to model evaluation metrics like RMSE and MAPE. Brush up on these concepts and be ready to discuss how you would evaluate the performance of your forecasting models and make improvements based on the results.
