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For Companies At a Glance
- Tasks: Join our innovation team to design and implement cutting-edge AI solutions.
- Company: Synergetic connects great companies with exceptional talent.
- Benefits: Enjoy a hybrid work model and the chance to work on impactful projects.
- Why this job: Be at the forefront of AI technology while collaborating with diverse teams.
- Qualifications: Strong background in data science, machine learning, and software engineering required.
- Other info: Contract position with opportunities to work on various 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 in your conversations. Highlight specific projects where you've successfully integrated AI solutions into production systems.
✨Tip Number 2
When reaching out to the job poster, mention any relevant experience you have with knowledge graphs and causal inference systems. This will demonstrate your understanding of the role's requirements and your ability to contribute effectively.
✨Tip Number 3
Discuss your familiarity with cloud platforms, especially Microsoft Azure, during your direct message. If you have experience with multiple cloud providers or container orchestration, be sure to include that as well.
✨Tip Number 4
Emphasize your programming skills in Python and any related data science libraries when communicating with the job poster. Providing examples of how you've used these skills in past projects can set you apart from other candidates.
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 for the Full Stack Data Scientist position. Understand the key responsibilities and required skills, especially focusing on machine learning, software engineering, and AI capabilities.
Tailor Your CV: Customize your CV to highlight your experience in data science and software engineering. Emphasize your expertise in machine learning, knowledge graphs, and any relevant projects that showcase your ability to scale AI solutions.
Craft a Compelling Cover Letter: Write a cover letter that connects your background with the specific requirements of the role. Mention your experience with cloud platforms, backend systems, and any familiarity with the industries mentioned in the job description.
Showcase Relevant Projects: Include examples of past projects where you developed and implemented machine learning models or AI solutions. Highlight your role in these projects and the impact they had on the business.
How to prepare for a job interview at Synergetic
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning models and software engineering. Highlight specific projects where you've developed and implemented AI solutions, especially those that involved causal ML and probabilistic modeling.
✨Demonstrate Collaboration Experience
Since the role involves working closely with cross-functional teams, share examples of how you've collaborated with backend engineers or other stakeholders to build scalable AI products. Emphasize your ability to translate business requirements into technical specifications.
✨Familiarize Yourself with Relevant Technologies
Brush up on your knowledge of cloud platforms, particularly Microsoft Azure, and be ready to discuss your experience with data pipelines, ETL processes, and microservices architecture. This will show that you understand the infrastructure needed for AI integration.
✨Prepare for Problem-Solving Questions
Expect to tackle complex business problems during the interview. Prepare to explain how you would approach creating probabilistic models or scaling AI solutions from proof-of-concept to production, demonstrating your analytical thinking and problem-solving skills.
