At a Glance
- Tasks: Build and test machine learning pipelines to drive business decisions.
- Company: Join the Financial Times, a leader in data-driven journalism.
- Benefits: Enjoy a hybrid work model, competitive salary, and flexible hours.
- Other info: Collaborative environment with opportunities for professional growth.
- Why this job: Make an impact on customer behaviour with your data science skills.
- Qualifications: Quantitative degree and proficiency in R or Python required.
The predicted salary is between 50000 - 70000 £ per year.
The Financial Times in London is seeking a Data Scientist to advance their applied data science initiatives. You will build and test machine learning pipelines and collaborate on projects that influence business decisions and customer behaviour.
The ideal candidate will hold a quantitative degree and possess skills in R or Python.
A hybrid working model allows flexibility with office attendance twice weekly, ensuring a balanced work-life environment.
Hybrid Data Scientist — ML Pipelines & Insights (London) employer: Financial Times
The Financial Times is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With a strong commitment to employee growth, you will have access to continuous learning opportunities while enjoying the flexibility of a hybrid working model that promotes a healthy work-life balance. Join us to make a meaningful impact on business decisions and customer insights in a supportive and forward-thinking environment.
StudySmarter Expert Advice🤫
We think this is how you could land Hybrid Data Scientist — ML Pipelines & Insights (London)
✨Tip Number 1
Network like a pro! Reach out to current or former employees at the Financial Times on LinkedIn. A friendly chat can give us insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your machine learning projects. We want to see how you’ve built and tested pipelines, so make it shine!
✨Tip Number 3
Practice makes perfect! Get ready for those technical interviews by brushing up on R or Python. We recommend doing mock interviews with friends or using online platforms.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take that extra step to connect directly with us.
We think you need these skills to ace Hybrid Data Scientist — ML Pipelines & Insights (London)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with machine learning pipelines and any relevant projects. We want to see how your skills in R or Python can contribute to our team!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data science and how you can influence business decisions. Be genuine and let your personality shine through!
Showcase Your Projects:If you've worked on any interesting data science projects, don’t hesitate to include them! We love seeing practical applications of your skills, especially those that demonstrate your problem-solving abilities.
Apply Through Our Website:For the best chance of getting noticed, make sure to apply directly through our website. It’s the easiest way for us to keep track of your application and get back to you quickly!
How to prepare for a job interview at Financial Times
✨Know Your Tech
Make sure you brush up on your R or Python skills before the interview. Be ready to discuss specific projects where you've built or tested machine learning pipelines, as this will show your practical experience and understanding of the tools.
✨Understand the Business
Research The Financial Times and their data initiatives. Familiarise yourself with how data science influences their business decisions and customer behaviour. This knowledge will help you tailor your answers and demonstrate your genuine interest in the role.
✨Prepare for Collaboration Questions
Since the role involves collaboration, think of examples from your past experiences where you worked effectively in a team. Be ready to discuss how you communicated complex data insights to non-technical stakeholders, as this is crucial for influencing decisions.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's data strategy and the specific projects you might be involved in. This not only shows your enthusiasm but also helps you gauge if the role aligns with your career goals and values.
