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- Tasks: Join our team to enhance Weather Data Cleaning Systems and develop machine learning capabilities.
- Company: Vaisala, a global leader in measurement instruments and intelligence.
- Benefits: Gain hands-on experience, training sessions, and build a valuable professional network.
- Why this job: Work on real-life projects that matter and make a difference for the planet.
- Qualifications: Experience with Python, machine learning libraries, and strong analytical skills.
- Other info: Dynamic environment with opportunities for career growth and innovation.
The predicted salary is between 500 - 1500 £ per month.
We’re looking for a Data Scientist Trainee for our Giant Leap program – a project‑based internship in our Harpenden Office, UK. You will join our Vaisala Xweather team specialised in Finance and Insurance to enhance our Weather Data Cleaning Systems. This position is central to our operations, focusing on the creation and maintenance of high‑quality datasets used by the weather risk management trading community.
As a Junior Data Scientist trainee or a Meteorologist with significant Data Scientist experience trainee, you will help in advancing our applied machine learning capabilities to clean Historical Weather Data. You will focus on exploring and developing new features, testing a variety of modeling approaches, and supporting innovation in how we use supervised learning for complex time‑series and structured data challenges.
Responsibilities:
- Develop, improve and automate Data Cleaning Techniques, ensuring high accuracy and reliability.
- Experiment with a range of modeling techniques for the various weather variables: temperature, rain, wind, etc.
- Collaborate with software developers to integrate promising approaches into larger workflows.
- Automate clients’ data cleaning techniques used as an input to ML models.
The role will be predominantly office based.
Qualifications:
- Knowledge of meteorological datasets.
- Hands‑on experience with machine learning libraries (scikit‑learn, XGBoost, PyTorch, TensorFlow, or similar).
- Familiarity with statistical methods.
- Proficiency in Python, with strong analytical and data manipulation skills.
- Familiarity with software development, ideally in C#.
- Excellent communication skills for liaising with internal teams and external stakeholders.
- While the core modelling is in Python, you will need to integrate your Python models/APIs with existing C#/.Net applications.
Giant Leap is a unique opportunity for you to be the project manager of your own project, with the support of your teammates and project supervisor. Every year, these projects are carefully selected by our leadership teams, meaning that you get to work on real‑life questions that are important for us as a company. We invest in your growth with training sessions throughout the summer. Along with lessons from your own field and work life in general, you have a chance to learn, for instance, presentation skills, project management and problem solving. Connections built with fellow Giant Leapers, Vaisala’s brilliant experts and our leadership form an invaluable network for your career. Many of our former Giant Leapers have also gone on to build impressive careers at Vaisala after the program.
Giant Leap Trainee, Data Scientist employer: Vaisala Oyj
Contact Detail:
Vaisala Oyj Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Giant Leap Trainee, Data Scientist
✨Tip Number 1
Network like a pro! Reach out to current or former employees on LinkedIn, especially those who have been through the Giant Leap programme. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a mini-project or a portfolio showcasing your data cleaning techniques and machine learning models. This will not only impress but also demonstrate your hands-on experience.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions related to data science and machine learning. Mock interviews with friends or mentors can help you articulate your thoughts clearly.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the Vaisala team.
We think you need these skills to ace Giant Leap Trainee, Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist Trainee role. Highlight relevant skills like Python proficiency, experience with machine learning libraries, and any projects that showcase your data cleaning techniques. We want to see how you can contribute to our Vaisala Xweather team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about the Giant Leap program and how your background aligns with our mission. Don’t forget to mention your passion for weather data and any innovative ideas you might have!
Showcase Your Projects: If you've worked on any relevant projects, whether in school or on your own, make sure to include them. We love seeing hands-on experience, especially with data cleaning and machine learning. It shows us you're ready to take that Giant Leap with us!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative to connect directly with us!
How to prepare for a job interview at Vaisala Oyj
✨Know Your Data Science Stuff
Make sure you brush up on your knowledge of machine learning libraries like scikit-learn and TensorFlow. Be ready to discuss how you've used these tools in the past, especially in relation to data cleaning techniques and modelling approaches.
✨Show Off Your Collaboration Skills
Since this role involves working closely with software developers, be prepared to talk about any previous experiences where you collaborated on projects. Highlight your communication skills and how you’ve successfully integrated different technologies, especially Python with C#.
✨Prepare for Technical Questions
Expect some technical questions related to statistical methods and data manipulation. Practise explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical stakeholders.
✨Demonstrate Your Passion for Weather Data
Show genuine enthusiasm for meteorological datasets and their applications. Share any relevant projects or experiences that highlight your interest in weather data and how it can impact industries like finance and insurance.
