Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead AI experiments from start to finish and design scalable AI systems.
- Company: Join a cutting-edge AI team focused on innovative solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact by bridging business needs with advanced AI technologies.
- Qualifications: Master's or Ph.D. in relevant fields and 5+ years of applied data science experience.
- Other info: Dynamic environment with a focus on collaboration and innovation.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking an innovative and highly skilled Generative AI Data Scientist to join our AI System Design team. This individual will play a key role in bridging business needs with advanced AI-driven solutions. The ideal candidate will have experience in end-to-end AI experiment life cycle management, architecture design, and the ability to translate business KPIs into measurable AI outcomes. A strong background in data management (preferably on Databricks), along with proficiency in modern Generative AI frameworks, is essential. This role demands both technical depth and excellent communication skills to engage with business stakeholders, design scalable AI systems, and ensure reliable model deployment using Azure and Databricks.
Key Responsibilities
- Lead end-to-end AI experimentation processes from conceptualization to deployment.
- Collaborate with business stakeholders to define success metrics, KPIs, and measurable outcomes for AI applications.
- Translate business requirements into detailed AI solution designs, including:
- Business Value definition
- AI solution design (Data Flow, AI component selection, Prompt Engineering, API, and AI integration planning)
- Automated model evaluation and monitoring frameworks
- PyTorch
- LangChain, LangGraph, Model Context Protocol, Google's Agent Development Kit (ADK), and similar agentic frameworks.
Required Qualifications
- Master's or Ph.D. in Computer Science, Data Science, AI/ML, Mathematics, or related field.
- 5+ years of experience in Applied data science, with at least 2+ years focused on Generative AI/LLMs.
- Strong experience in Databricks data engineering and ML workflows.
- Proficiency in the Azure AI/ML ecosystem, including MLOps and data management best practices.
- Demonstrated expertise in:
- Python (advanced)
- AI/ML libraries (PyTorch, Hugging Face, Scikit-learn, TensorFlow, optional)
- Prompt engineering and fine-tuning LLMs for task-specific use cases
Generative AI Data Scientist employer: Careerwise
Contact Detail:
Careerwise Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Generative AI Data Scientist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, experiments, and any cool stuff you've built. This is your chance to demonstrate your expertise in Generative AI and data management, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and communication skills. Be ready to discuss how you can bridge business needs with AI solutions, and don’t forget to practice explaining complex concepts in simple terms.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight your experience with Databricks and Azure, and let us know how you can contribute to our AI System Design team.
We think you need these skills to ace Generative AI Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Generative AI Data Scientist. Highlight your experience with AI experiment life cycles, data management on Databricks, and any relevant projects that showcase your skills in Generative AI frameworks.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're the perfect fit for our AI System Design team. Share specific examples of how you've bridged business needs with AI solutions and how you’ve translated KPIs into measurable outcomes.
Showcase Your Technical Skills: Don’t hold back on your technical prowess! Mention your experience with Python, Azure, and any AI/ML libraries you’ve worked with. We want to see your familiarity with tools like PyTorch and Hugging Face, so make it clear!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and get you in front of the right people quickly!
How to prepare for a job interview at Careerwise
✨Know Your Generative AI Inside Out
Make sure you brush up on the latest trends and techniques in Generative AI. Be ready to discuss your experience with frameworks like PyTorch and LangChain, and how you've applied them in real-world scenarios. This will show that you're not just familiar with the tools, but that you can leverage them effectively.
✨Speak Their Language
Since this role involves collaborating with business stakeholders, practice translating complex AI concepts into simple terms. Prepare examples of how you've defined success metrics and KPIs in past projects. This will demonstrate your ability to bridge the gap between technical and non-technical audiences.
✨Showcase Your End-to-End Process
Be ready to walk through your experience managing the entire AI experiment life cycle. Highlight specific projects where you led from conceptualisation to deployment, focusing on the challenges you faced and how you overcame them. This will illustrate your hands-on experience and problem-solving skills.
✨Prepare for Technical Questions
Expect to dive deep into your technical expertise during the interview. Brush up on your knowledge of data pipelines, model evaluation frameworks, and Azure ML workflows. Practising coding problems or discussing your approach to fine-tuning LLMs can also give you an edge.
