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For Companies At a Glance
- Tasks: Lead the development of time series forecasting models for ad revenue and digital performance.
- Company: Join a leading digital media organisation focused on data-driven growth.
- Benefits: Enjoy hybrid working, competitive pay, and a modern tech stack.
- Why this job: Make a measurable impact from day one in a dynamic environment.
- Qualifications: 5+ years in time series forecasting, strong Python and SQL skills required.
- Other info: 6-month contract with potential for extension; work with cross-functional teams.
This is an exciting opportunity for a Data Scientist to lead the development of robust time series forecasting models for a major media organisation. You will take ownership of a fully scoped, delivery-focused project, working across daily, weekly, and monthly resolutions to forecast ad revenue, website traffic, and digital performance. The environment offers autonomy, technical depth, and the chance to make a measurable business impact from day one.
THE COMPANY
A leading name in the digital media space, this organisation is focused on leveraging data to drive growth and performance across its digital channels. With a modern cloud-based tech stack and a growing demand for predictive insights, they are investing in scalable data science capabilities. Based in London, the team operates on a hybrid model with 2 days a week in their office.
THE ROLE
As the lead Data Scientist on this project, you will be responsible for the end-to-end delivery of forecasting solutions, from model design to deployment. You will collaborate closely with stakeholders across marketing, commercial, and product teams to shape model inputs and outputs that are both business-relevant and technically sound.
Your responsibilities will include:
- Building and deploying time series models for revenue, traffic, and ad performance
- Operating across multiple temporal resolutions (daily, weekly, monthly)
- Working with structured web analytics and revenue data in a cloud environment
- Ensuring models are robust, explainable, and scalable for production use
- Collaborating with cross-functional teams to understand needs and define targets
- Managing delivery independently within a scoped project timeline
KEY SKILLS AND REQUIREMENTS
- 5+ years' experience delivering commercial time series forecasting projects
- Strong programming ability in Python (NumPy, Pandas, scikit-learn)
- Solid SQL skills and experience working with large datasets
- Experience deploying models into production environments (GCP preferred)
- Strong communication skills and stakeholder collaboration experience
- Ability to manage the full delivery lifecycle autonomously
TECH STACK
- Python, SQL
- GCP (BigQuery, Cloud Functions)
- Docker, Kubernetes, Airflow
- Git, CI/CD pipelines
- Tableau (optional)
WHY APPLY
- Lead a high-impact forecasting project for a major media brand
- Work with a modern, cloud-first tech stack
- Hybrid working: £600/day (Inside IR35, up to £650 billing rate)
- 6-month initial contract
HOW TO APPLY
Please register your interest by sending your CV via the apply link on this page.
Data Scientist - Time Series Forecasting employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Time Series Forecasting
✨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 or Prophet in a commercial context can set you apart during discussions.
✨Tip Number 2
Brush up on your Python skills, especially with libraries like NumPy and Pandas. Being able to demonstrate your coding proficiency in these areas during interviews will show that you're ready to hit the ground running.
✨Tip Number 3
Prepare to discuss your experience with deploying models into production, particularly in cloud environments like GCP. Having concrete examples of past projects where you've successfully managed this process will be crucial.
✨Tip Number 4
Highlight your ability to collaborate with cross-functional teams. Be ready to share examples of how you've worked with stakeholders from different departments to ensure that your models meet business needs.
We think you need these skills to ace Data Scientist - Time Series Forecasting
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in time series forecasting and showcases your programming skills in Python and SQL. Emphasise any previous projects where you've deployed models into production, especially in a cloud environment.
Craft a Compelling Cover Letter: Write a cover letter that specifically addresses the key skills and requirements mentioned in the job description. Discuss your experience with commercial time series forecasting projects and how you can contribute to the company's goals.
Showcase Technical Skills: In your application, clearly outline your technical skills, particularly in Python libraries like NumPy and Pandas, as well as your experience with GCP and data deployment. This will demonstrate your capability to handle the responsibilities of the role.
Highlight Collaboration Experience: Since the role involves working closely with cross-functional teams, include examples of past collaborations in your application. Describe how you effectively communicated with stakeholders to shape project outcomes.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Make sure to highlight your experience with Python, SQL, and any relevant libraries like NumPy and Pandas. Be prepared to discuss specific projects where you've successfully deployed time series forecasting models.
✨Understand the Business Context
Familiarise yourself with the media industry and how data impacts ad revenue and digital performance. Being able to connect your technical skills to business outcomes will impress the interviewers.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about how you would approach building a forecasting model from scratch and be ready to explain your thought process.
✨Demonstrate Collaboration Skills
Since the role involves working with cross-functional teams, be ready to share examples of how you've effectively collaborated with stakeholders in previous projects. Highlight your communication skills and ability to manage expectations.
