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- Tasks: Lead a team of data scientists and drive machine learning engineering initiatives.
- Company: High-growth business in the UK with a focus on innovation.
- Benefits: Competitive salary of £115K - £125K plus bonus.
- Why this job: Shape the future of data strategy and make a significant impact.
- Qualifications: Proven leadership in data science and expertise in ML model deployment.
- Other info: Hybrid role based in Manchester with exciting growth opportunities.
The predicted salary is between 115000 - 125000 £ per year.
A high-growth business in the United Kingdom seeks a Head of Data Science to lead a team of data scientists and drive the transition towards machine learning engineering. This role includes setting technical direction, ensuring alignment with company objectives, and shaping the data strategy.
Candidates should have proven leadership in data science, strong expertise in ML model deployment, and the ability to partner with stakeholders.
A competitive salary of £115K - £125K plus bonus is offered.
Head of Data Science & ML Engineering (Hybrid, Manchester) in London employer: Data Idols
Contact Detail:
Data Idols Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Head of Data Science & ML Engineering (Hybrid, Manchester) in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science and ML engineering fields. Attend industry meetups or webinars to meet potential employers and showcase your expertise.
✨Tip Number 2
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your past projects. We recommend practising common interview questions related to data strategy and ML model deployment.
✨Tip Number 3
Showcase your leadership skills! Be prepared to share examples of how you've led teams and driven successful projects. Highlight your ability to align data initiatives with business objectives.
✨Tip Number 4
Apply through our website! We make it easy for you to find roles that match your skills. Plus, it shows you're serious about joining our high-growth team in Manchester.
We think you need these skills to ace Head of Data Science & ML Engineering (Hybrid, Manchester) in London
Some tips for your application 🫡
Show Your Leadership Skills: As we're looking for someone to lead our data science team, make sure to highlight your previous leadership experiences. Share specific examples of how you've guided teams and driven projects to success.
Demonstrate Your Technical Expertise: We want to see your strong background in ML model deployment. Include details about the technologies and methodologies you've used, and how they align with our goals at StudySmarter.
Align with Our Objectives: Make it clear how your vision for data strategy aligns with our company objectives. Show us that you understand our mission and how you can contribute to it through your role.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team.
How to prepare for a job interview at Data Idols
✨Know Your Data Science Stuff
Make sure you brush up on your data science fundamentals and machine learning concepts. Be ready to discuss your past projects, especially those involving ML model deployment, as this will show your hands-on experience and technical expertise.
✨Show Leadership Skills
Prepare examples that highlight your leadership experience in data science. Think about how you've led teams, managed projects, or influenced stakeholders. This is crucial since the role requires someone who can guide a team and align with company objectives.
✨Understand the Business Context
Research the company and its goals. Understand how data science and machine learning can drive their business forward. Being able to articulate how your vision aligns with their objectives will set you apart from other candidates.
✨Prepare for Stakeholder Engagement
Since the role involves partnering with stakeholders, think about how you’ve successfully communicated complex data concepts to non-technical audiences. Prepare to discuss strategies for collaboration and how you can bridge the gap between technical and non-technical teams.
