Hybrid Data Scientist β€” ML, Data Pipelines & Impact in London

Hybrid Data Scientist β€” ML, Data Pipelines & Impact in London

London Full-Time 63000 - 77000 Β£ / year (est.) Home office (partial)
Kwiff

At a Glance

  • Tasks: Create data science solutions that deliver real business impact using analytics and machine learning.
  • Company: Join kwiff, a forward-thinking company focused on innovation and collaboration.
  • Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
  • Other info: Be part of a dynamic team with exciting projects and career advancement potential.
  • Why this job: Make a difference by solving real-world problems with data-driven solutions.
  • Qualifications: Experience in data science, machine learning, and strong problem-solving skills.

The predicted salary is between 63000 - 77000 Β£ per year.

kwiff is seeking a Data Scientist to develop data science solutions that drive measurable business impact.

You will work in the Data team, using analytics, ML and data engineering to solve problems from concept to production deployment.

You will collaborate with commercial stakeholders to understand challenges and translate them into scalable, data-driven solutions while advancing kwiff's data science capabilities.

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Hybrid Data Scientist β€” ML, Data Pipelines & Impact in London employer: Kwiff

At kwiff, we pride ourselves on being a forward-thinking employer that champions innovation and inclusivity in the iGaming industry. Our vibrant work culture fosters personal and professional growth, offering comprehensive benefits such as private healthcare, performance bonuses, and a learning budget to enhance your skills. With a hybrid working model and regular team socials, you'll thrive in an environment that values your contributions while redefining the sports betting experience in London.

Kwiff

Contact Details:

Kwiff Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land Hybrid Data Scientist β€” ML, Data Pipelines & Impact in London

✨Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Kwiff!

✨Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Hybrid Data Scientist β€” ML, Data Pipelines & Impact at Kwiff.

✨Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Kwiff.

✨Apply Directly through Our Website

When you find a suitable opening like Hybrid Data Scientist β€” ML, Data Pipelines & Impact at Kwiff, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Hybrid Data Scientist β€” ML, Data Pipelines & Impact in London

Data Science
Machine Learning (ML)
Data Engineering
Analytics
Problem-Solving Skills
Collaboration
Scalable Solutions

Some tips for your application 🫑

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Kwiff, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Kwiff. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Kwiff

✨Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

✨Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

✨Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Kwiff!

✨Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.