At a Glance
- Tasks: Join us as a Research Data Scientist to tackle complex business problems using machine learning and data analysis.
- Company: dunnhumby is a global leader in Customer Data Science, empowering businesses to thrive in a data-driven economy.
- Benefits: Enjoy flexible working hours, your birthday off, and a comprehensive rewards package.
- Why this job: Work with passionate experts, innovate solutions, and make impactful decisions in category management.
- Qualifications: PhD in relevant fields and experience with machine learning techniques and programming, preferably Python.
- Other info: We embrace diversity and inclusion, offering a supportive environment for all applicants.
The predicted salary is between 28800 - 48000 £ per year.
dunnhumby is the global leader in Customer Data Science, empowering businesses everywhere to compete and thrive in the modern data-driven economy. We always put the Customer First. Our mission: to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail – one of the world’s most competitive markets, with a deluge of multi-dimensional data – dunnhumby today enables businesses all over the world, across industries, to be Customer First.
dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Meijer, Procter & Gamble and Metro. Joining our team, you’ll work with world-class and passionate people to apply machine learning and statistical techniques to business problems. You’ll contribute to the research and implementation of new approaches to address complex problems and perform data analysis and model validation. You’ll have the opportunity to present results to a variety of internal stakeholders.
You will apply these techniques and algorithms to create dunnhumby science solutions that can be delivered across our clients and engineered into science modules. This role will be focused on how we ensure better decisions are made as part of category management, ensuring the right product is in the hands of the customer, by enhancing our data-led understanding of products and categories, optimizing across space and range and increasing automation of category decision making.
What we expect from you:
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
- Experience with machine learning techniques such as regularized regression, clustering or tree-based ensembles, and the ability to implement them through libraries.
- Experience with programming, ideally Python, and the ability to quickly pick up handling large data volumes with modern data processing tools, e.g. by using Hadoop / Spark / SQL.
- Experience with or ability to quickly learn open-source software including machine learning packages, such as Pandas, scikit-learn, along with data visualization technologies.
- Experience in the retail sector would be an added advantage.
What you can expect from us:
We won’t just meet your expectations. We’ll defy them. So you’ll enjoy the comprehensive rewards package you’d expect from a leading technology company. But also, a degree of personal flexibility you might not expect. Plus, thoughtful perks, like flexible working hours and your birthday off. You’ll also benefit from an investment in cutting-edge technology that reflects our global ambition. But with a nimble, small-business feel that gives you the freedom to play, experiment and learn.
And we don’t just talk about diversity and inclusion. We live it every day – with thriving networks including dh Gender Equality Network, dh Proud, dh Family, dh One and dh Thrive as the living proof. We want everyone to have the opportunity to shine and perform at your best throughout our recruitment process. Please let us know how we can make this process work best for you.
Our approach to Flexible Working:
At dunnhumby, we value and respect difference and are committed to building an inclusive culture by creating an environment where you can balance a successful career with your commitments and interests outside of work. We believe that you will do your best at work if you have a work/life balance. Some roles lend themselves to flexible options more than others, so if this is important to you please raise this with your recruiter, as we are open to discussing agile working opportunities during the hiring process.
Research Data Scientist employer: RATA TRANSPORTATION LLC
Contact Detail:
RATA TRANSPORTATION LLC Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Data Scientist
✨Tip Number 1
Familiarise yourself with dunnhumby's mission and values. Understanding their focus on being 'Customer First' will help you align your discussions and demonstrate how your skills can contribute to their goals during interviews.
✨Tip Number 2
Brush up on your machine learning techniques, especially those mentioned in the job description like regularized regression and tree-based ensembles. Be prepared to discuss specific projects where you've applied these methods, as practical examples will showcase your expertise.
✨Tip Number 3
Network with current or former employees of dunnhumby on platforms like LinkedIn. Engaging with them can provide insights into the company culture and expectations, which can be invaluable during your interview process.
✨Tip Number 4
Prepare to discuss your experience with data processing tools like Hadoop, Spark, and SQL. Being able to articulate how you've handled large data volumes in past roles will demonstrate your readiness for the challenges you'll face as a Research Data Scientist.
We think you need these skills to ace Research Data Scientist
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Research Data Scientist position at dunnhumby. Familiarise yourself with their focus on customer data science and how your skills align with their mission.
Tailor Your CV: Customise your CV to highlight relevant experience in machine learning, programming (especially Python), and data analysis. Emphasise any experience you have in the retail sector, as this will be advantageous.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for customer data science and your understanding of dunnhumby's mission. Mention specific projects or experiences that demonstrate your ability to apply machine learning techniques to solve complex problems.
Showcase Your Skills: In your application, provide examples of your experience with machine learning libraries like Pandas and scikit-learn. If possible, include links to any relevant projects or portfolios that demonstrate your expertise in handling large data volumes and data visualisation.
How to prepare for a job interview at RATA TRANSPORTATION LLC
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning techniques and programming languages, especially Python. Highlight specific projects where you've implemented algorithms or handled large datasets, as this will demonstrate your practical knowledge.
✨Understand the Company’s Mission
Familiarise yourself with dunnhumby’s focus on Customer Data Science and their commitment to putting customers first. Be ready to discuss how your skills can contribute to their mission of enabling businesses to grow through data-driven insights.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your analytical thinking and problem-solving abilities. Practice explaining your thought process when tackling complex data problems, as this will showcase your critical thinking skills.
✨Engage with Stakeholders
Since the role involves presenting results to various internal stakeholders, prepare to discuss how you would communicate complex data findings in an understandable way. Think about examples where you've successfully conveyed technical information to non-technical audiences.