At a Glance
- Tasks: Define and guide AI and data science strategy across machine learning, NLP, and generative AI.
- Company: Elsevier B.V. is a leading provider of information solutions in the academic sector.
- Benefits: Enjoy flexible working hours and various benefits for a healthy work-life balance.
- Other info: This role involves driving complex engineering efforts.
- Why this job: Lead impactful AI solutions that align with product goals and customer needs.
- Qualifications: Extensive experience in AI and data science with strong leadership skills required.
The predicted salary is between 80000 - 100000 £ per year.
Elsevier B.V. is looking for a leader to define and guide AI and data science strategy across machine learning, NLP, search, and generative AI. You will be responsible for delivering impactful AI solutions, aligning team efforts with product goals and customer needs.
The ideal candidate will have extensive experience in AI and data science, strong leadership skills, and the ability to drive complex engineering efforts. The role promises flexibility in working hours and various benefits to support a healthy work-life balance.
Head of AI & Data Science — Strategy & Scale in London employer: Elsevier B.V.
Elsevier B.V. offers a dynamic environment focused on innovation in academic information solutions. Located in Amsterdam, the company promotes a healthy work-life balance with flexible hours and comprehensive benefits. Join a team dedicated to advancing AI and data science in meaningful ways.
StudySmarter Expert Advice🤫
We think this is how you could land Head of AI & Data Science — Strategy & Scale in London
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We think you need these skills to ace Head of AI & Data Science — Strategy & Scale in London
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!
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Craft a Tailored Cover Letter:For a full-time role at Elsevier B.V., 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 Elsevier B.V.. 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 Elsevier B.V.
✨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!
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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 Elsevier B.V.!
✨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.
