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- Tasks: Build data foundations and predictive models that impact healthcare outcomes.
- Company: Join a leading research-driven pharmaceutical company with a rich 140-year heritage.
- Benefits: Competitive salary, company car, and opportunities for professional growth.
- Other info: Experience a start-up vibe with the resources of a global leader.
- Why this job: Transform your data engineering skills into impactful data science work.
- Qualifications: Strong SQL, Python, Spark/PySpark skills and a quantitative degree.
The predicted salary is between 60000 - 67000 £ per year.
Winnersh - 2 days onsite at Winnersh Triangle - 45 mins by train from Paddington £60,000–£67,000 (depending on experience) + Company Car
Build The Data Foundation. Then Build Predictive Models.
Most data engineering roles stay data engineering roles. This one won’t. You studied maths, stats, physics, or maybe even Operations Research. You only took a data engineering job because it was the sensible move and there weren’t many opportunities available to become a Data Scientist. But deep down, you know you’re capable of more than just keeping the data flowing. You want to build models and get back to your quant core. You’re just waiting for the role that takes you there. This is that role.
Year one is about building something real and consequential – the data foundation for the entire UK & Ireland commercial operation on Databricks. Building robust automated data pipelines and preparing datasets for downstream analytics and data science. Automated data quality checks and reporting will also be key, ensuring the foundation is rock-solid.
Year two is about using that foundation for genuinely advanced work: predictive modelling, NLP on clinical data, and AI pre/post-call agents powered by LLMs. Customer segmentation using clustering models and Monte Carlo simulations will also feature.
This is not just a dashboarding team. Their work is end-to-end, which is rare for a large organisation. It’s more similar to a start up, but with resources and business opportunities that start ups can rarely match.
What you’ll need:
- Strong SQL, Python, and Spark/PySpark skills
- Experience in data pipeline design
- A quantitative degree - Maths, Stats, Physics, Operations research
About the company: You’ll be working for one of the world’s leading research-driven pharmaceutical companies, with 140 years of heritage. They have over 50k employees and operations spanning 76 countries and are the largest privately owned Pharma company in the world. As a family-owned business, they take a long-term view - investing in breakthrough innovations that improve the lives of people and animals around the globe. From developing treatments for chronic diseases to advancing computational biology, their work has a tangible impact on healthcare outcomes worldwide.
Please click the ‘Apply’ button to find out more.
Data Engineer/Scientist - Life Sciences in Reading employer: Data Science Talent
Contact Detail:
Data Science Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer/Scientist - Life Sciences in Reading
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those involving SQL, Python, and Spark. This will give you an edge and demonstrate your capability to build predictive models and data pipelines.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with data engineering and how it can transition into data science roles. Practice common interview questions to boost your confidence!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search. So, hit that ‘Apply’ button and let’s get you started on this exciting journey!
We think you need these skills to ace Data Engineer/Scientist - Life Sciences in Reading
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your SQL, Python, and Spark/PySpark skills in your application. We want to see how you can use these tools to build robust data pipelines and contribute to our data foundation.
Quantitative Background Matters: Don’t forget to mention your quantitative degree! Whether it’s Maths, Stats, Physics, or Operations Research, we’re keen to see how your academic background aligns with the role and how it prepares you for advanced modelling work.
Be Genuine About Your Ambitions: Let us know why you’re excited about moving from data engineering to data science. We’re looking for candidates who are eager to build predictive models and dive into advanced analytics, so share your passion!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and get you on the path to joining our innovative team!
How to prepare for a job interview at Data Science Talent
✨Know Your Tech Stack
Make sure you brush up on your SQL, Python, and Spark/PySpark skills. Be ready to discuss specific projects where you've used these technologies, as well as any challenges you faced and how you overcame them.
✨Showcase Your Quantitative Skills
Since this role is all about building predictive models, be prepared to talk about your quantitative background. Bring examples from your studies or previous work that highlight your ability to analyse data and derive insights.
✨Understand the Company’s Mission
Familiarise yourself with the company’s focus on research-driven innovations in healthcare. Being able to articulate how your role as a Data Engineer/Scientist contributes to their mission will show your genuine interest and alignment with their goals.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities. Think of scenarios where you had to design data pipelines or ensure data quality. Practising these types of questions can help you articulate your thought process clearly during the interview.