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- Tasks: Design and implement advanced AI solutions while collaborating with cross-functional teams.
- Company: Join an innovative team focused on cutting-edge AI and knowledge systems.
- Benefits: Enjoy a hybrid work model with flexibility and the chance to work in London.
- Why this job: Be at the forefront of AI technology, transforming concepts into scalable products.
- Qualifications: Strong background in data science, machine learning, and software engineering required.
- Other info: Opportunity to work with diverse industries like Financial Services and Healthcare.
The predicted salary is between 48000 - 72000 £ per year.
Full Stack Data Scientist – AI & Knowledge Systems
3-6 month contract
Outside IR35
Hybrid (2-3 days per week in London)
About the Role
We are seeking an exceptional Full Stack Data Scientist to join our clients innovation team. This role combines traditional data science expertise with software engineering capabilities to build end-to-end AI solutions. The ideal candidate will have a strong foundation in both developing sophisticated machine learning models and implementing them within production systems. You will work closely with cross-functional teams to transform concepts into scalable AI-powered products.
Responsibilities
- Design, develop, and implement advanced machine learning models and AI capabilities
- Build and maintain knowledge graphs and causal inference systems
- Create probabilistic models to address complex business problems
- Scale AI solutions from proof-of-concept to MVP and full production
- Collaborate with backend engineers on data pipelines and infrastructure
- Work within solution architecture frameworks to ensure AI integration
- Contribute to solution design and technical decision-making
- Translate business requirements into technical specifications
Required Skills & Experience
- Extensive experience combining data science with software engineering
- Strong expertise in machine learning, with focus on causal ML and probabilistic modelling
- Experience developing and implementing knowledge graphs
- Proficiency in scaling AI solutions from concept to production
- Working knowledge of backend systems, data pipelines, and ETL processes
- Familiarity with cloud platforms, particularly Microsoft Azure
- Understanding of microservices architecture and distributed systems
- Experience with DevOps practices for AI/ML workflows (MLOps)
- Strong programming skills in Python and related data science libraries
- Demonstrated ability to work within solution architecture frameworks
Other preferred skills
- Experience with multiple cloud providers beyond Azure
- Familiarity with container orchestration (Kubernetes)
- Knowledge of graph databases and query languages
- Experience with deep learning frameworks
- Background in NLP, computer vision, or reinforcement learning
- Domain expertise across industries but familiar with Financial Services, Healthcare and Lifesciences, Industrials and Telecommunications and infrastructure would be a plus
Data Scientist employer: Synergetic
Contact Detail:
Synergetic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Make sure to showcase your experience with both machine learning and software engineering. Highlight specific projects where you've developed and implemented AI solutions, especially those that involved scaling from proof-of-concept to production.
✨Tip Number 2
Familiarize yourself with the latest trends in causal ML and probabilistic modeling. Being able to discuss recent advancements or case studies in these areas during your interview can set you apart from other candidates.
✨Tip Number 3
Since collaboration is key in this role, prepare examples of how you've successfully worked with cross-functional teams in the past. Emphasize your ability to translate business requirements into technical specifications.
✨Tip Number 4
Brush up on your knowledge of cloud platforms, particularly Microsoft Azure, and be ready to discuss your experience with data pipelines and ETL processes. This will demonstrate your readiness to integrate AI solutions within existing infrastructures.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description and understand the key responsibilities and required skills. Tailor your application to highlight your experience in machine learning, software engineering, and AI solutions.
Highlight Relevant Experience: In your CV and cover letter, emphasize your extensive experience in data science and software engineering. Provide specific examples of projects where you developed machine learning models or implemented knowledge graphs.
Showcase Technical Skills: Clearly list your programming skills, particularly in Python and any relevant libraries. Mention your familiarity with cloud platforms like Microsoft Azure and your understanding of microservices architecture and DevOps practices.
Tailor Your Cover Letter: Write a compelling cover letter that connects your background to the specific needs of the role. Discuss how your experience aligns with the company's goals and how you can contribute to their innovation team.
How to prepare for a job interview at Synergetic
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning models, knowledge graphs, and AI capabilities. Highlight specific projects where you've successfully implemented these technologies, especially in production environments.
✨Demonstrate Collaboration Experience
Since this role involves working closely with cross-functional teams, share examples of how you've collaborated with backend engineers or other stakeholders. Emphasize your ability to translate business requirements into technical specifications.
✨Discuss Your Problem-Solving Approach
Prepare to talk about complex business problems you've addressed using probabilistic models or causal inference systems. Explain your thought process and the impact of your solutions on the organization.
✨Familiarize Yourself with Their Tech Stack
Research the company's use of cloud platforms, microservices architecture, and DevOps practices. Being knowledgeable about their specific tools and frameworks will show your genuine interest and readiness to integrate into their team.
