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For Companies At a Glance
- Tasks: Lead data science strategy and collaborate with teams to solve client challenges.
- Company: Join a forward-thinking company focused on responsible AI and innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Why this job: Shape the future of AI while making a real impact in diverse client contexts.
- Qualifications: Strong background in data science and experience with cloud platforms.
- Other info: Dynamic role with a focus on continuous improvement and responsible AI practices.
The predicted salary is between 43200 - 72000 £ per year.
Core Responsibilities
- Shape Data Science Strategy: Define and advise on the data science approach for your product, ensuring a balance of analytical rigor, interpretability, and scalability, while enabling model reuse across multiple client contexts.
- Client Engagement: Collaborate with sector teams, go-to-market specialists, and solution architects to uncover client challenges, showcase product capabilities, gather feedback, and influence development priorities.
- Model Deployment: Work closely with engineers to productionize models on cloud platforms (Azure, AWS, or GCP) using MLOps and DevSecOps best practices.
- Continuous Improvement: Partner with the Product Owner to monitor model performance and user feedback, refining algorithms, enhancing features, and driving better product outcomes over time.
- Responsible AI: Embed principles of responsible and explainable AI throughout development to ensure outputs are trusted, transparent, and compliant with PwC standards.
Head of Data Science employer: Experis
Contact Detail:
Experis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Head of Data Science
✨Tip Number 1
Network like a pro! Reach out to people in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Showcase your skills! Create a portfolio or a GitHub repository with your projects. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data science. We recommend doing mock interviews with friends or using online platforms to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s a great way to ensure your application gets seen, and you might find exclusive roles that fit your skills perfectly.
We think you need these skills to ace Head of Data Science
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the Head of Data Science role. Highlight your experience with data science strategies, model deployment, and client engagement. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you're passionate about data science and how you can contribute to our team. Be sure to mention any relevant projects or experiences that showcase your expertise.
Showcase Your Technical Skills: Don’t forget to highlight your technical skills in your application. Mention your experience with cloud platforms like Azure, AWS, or GCP, and any MLOps practices you've implemented. We love seeing candidates who are hands-on with the latest tech!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Experis
✨Know Your Data Science Strategy
Before the interview, brush up on your understanding of data science strategies. Be ready to discuss how you would define and implement a robust approach that balances analytical rigor with interpretability. Think about examples from your past experiences where you've successfully shaped data strategies.
✨Engage with Client Scenarios
Prepare to talk about how you've collaborated with different teams to address client challenges. Have specific examples ready that showcase your ability to gather feedback and influence product development. This will demonstrate your client engagement skills and your understanding of market needs.
✨Model Deployment Know-How
Familiarise yourself with MLOps and DevSecOps best practices, especially in relation to cloud platforms like Azure, AWS, or GCP. Be prepared to discuss your experience in productionising models and how you ensure smooth deployment processes. This shows you’re not just theoretical but practical too.
✨Champion Responsible AI
Understand the principles of responsible and explainable AI. Be ready to discuss how you’ve embedded these principles in your previous work. Highlight any experiences where you ensured compliance and transparency in your projects, as this is crucial for building trust in AI outputs.
