At a Glance
- Tasks: Analyse complex datasets and build predictive models to drive data-driven decisions.
- Company: Join a forward-thinking marketing team in a dynamic tech environment.
- Benefits: Enjoy competitive pay, remote work options, and opportunities for professional growth.
- Other info: Collaborative culture with continuous learning and career advancement opportunities.
- Why this job: Make a real impact by transforming data into actionable insights that enhance customer experiences.
- Qualifications: Degree in a quantitative field; familiarity with Python, R, or SQL is a plus.
The predicted salary is between 28000 - 35000 € per year.
To support the organisation’s data‑driven decision‑making by analysing complex datasets, building predictive models and generating actionable insights. Work closely with senior data scientists and business stakeholders to ensure insights enable decisions that optimise processes, improve customer experiences and contribute to commercial growth.
Key Responsibilities
- Assist in collecting, cleaning and preparing data from various sources for analysis and modelling.
- Work on well‑defined data science problems with clear success criteria.
- Build, test and deploy basic predictive models under senior guidance.
- Support development of dashboards and automated reports for business stakeholders.
- Contribute to A/B testing and experimentation to improve product features and customer journeys.
- Collaborate with cross‑functional teams (analytics, marketing, product, operations) to understand requirements and deliver data solutions.
- Present findings and insights clearly to both technical and non‑technical audiences.
- Continuously learn new tools, techniques and best practices in data science and analytics.
Strategic Responsibility
The Junior Data Scientist supports the development and implementation of analytical solutions, providing valuable input through data exploration, model building and insight generation that inform strategic decisions.
Business Knowledge
Works with stakeholders to identify business problems, build data‑science solutions and identify value metrics that drive adoption.
Problem Solving
Regularly tackles data‑related challenges, using logical reasoning and statistical techniques, and experiments with modelling approaches to improve solution accuracy.
Decision Making
Makes decisions within defined procedures and guidelines, such as selecting appropriate data cleaning methods or basic modelling techniques, and escalates complex decisions to senior data scientists or managers.
Communication
Communicates findings and technical concepts clearly to team members and stakeholders, prepares visualisations and reports accessible to non‑technical audiences, listens actively and adapts communication style as needed.
Innovation
Contributes ideas for improving data processes, reporting and modelling approaches, keeps up to date with emerging tools and techniques, and participates in brainstorming sessions to drive continuous improvement.
Qualifications
Education: A degree in a quantitative field such as Mathematics, Statistics, Computer Science or Engineering is preferred. Certifications in data science, analytics or programming languages (Python, R) are advantageous but not essential.
Knowledge: Understanding of statistical concepts and data analysis techniques; familiarity with data manipulation and visualisation tools (Python, R, SQL, Excel, Tableau); awareness of machine learning fundamentals and common algorithms; knowledge of data governance and privacy standards (GDPR).
Skills/Abilities: Analytical thinking and problem‑solving; ability to work with large datasets and perform data cleaning and transformation; programming skills in Python or R; experience with data visualisation tools (Tableau, Power BI, Matplotlib); strong attention to detail and accuracy; willingness to learn and adapt to new technologies; effective communication and presentation skills; ability to work collaboratively in a team environment; time management and organisational skills.
Junior Data Scientist / Data Scientist (entry-level) employer: hackajob
As a Junior Data Scientist at our company, you will thrive in a dynamic and supportive work environment that prioritises employee growth and development. With opportunities to collaborate with experienced professionals in the heart of Milton Keynes or remotely from Manchester, you will gain hands-on experience in data analysis and modelling while contributing to impactful projects that drive business success. Our culture fosters innovation and continuous learning, ensuring that you are well-equipped to advance your career in the exciting field of data science.
StudySmarter Expert Advice🤫
We think this is how you could land Junior Data Scientist / Data Scientist (entry-level)
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with fellow data enthusiasts on LinkedIn. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data analysis and predictive modelling. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data science questions and practising your problem-solving skills. Be ready to discuss your thought process and how you tackle data challenges – it’s all about demonstrating your analytical mindset!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for fresh talent, and applying directly can sometimes give you an edge. Plus, it shows you’re genuinely interested in joining our team!
We think you need these skills to ace Junior Data Scientist / Data Scientist (entry-level)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Junior Data Scientist role. Highlight any relevant projects or coursework, especially those involving data analysis, programming, or statistical techniques.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data science and how your background makes you a great fit for our team. Be sure to mention specific tools or techniques you've used that align with the job description.
Showcase Your Projects:If you've worked on any data-related projects, whether in school or on your own, include them in your application. We love seeing practical examples of your skills, so don’t hold back!
Apply Through Our Website:For the best chance of getting noticed, make sure to apply directly through our website. It helps us keep track of applications and ensures you’re considered for the role!
How to prepare for a job interview at hackajob
✨Know Your Data Tools
Make sure you brush up on your knowledge of data manipulation and visualisation tools like Python, R, SQL, and Tableau. Be ready to discuss how you've used these tools in your studies or projects, as this will show your practical understanding and readiness to dive into the role.
✨Prepare for Problem-Solving Questions
Expect questions that test your analytical thinking and problem-solving skills. Think of examples where you've tackled data-related challenges, and be prepared to explain your thought process and the techniques you used to arrive at a solution.
✨Communicate Clearly
Since you'll need to present findings to both technical and non-technical audiences, practice explaining complex concepts in simple terms. Use visuals if possible, and be ready to adapt your communication style based on who you're speaking to during the interview.
✨Show Your Willingness to Learn
Highlight your eagerness to learn new tools and techniques in data science. Share any recent courses, certifications, or personal projects you've undertaken to stay updated in the field. This will demonstrate your commitment to continuous improvement and innovation.
