At a Glance
- Tasks: Design and build AI/ML solutions for real-world problems using cutting-edge technologies.
- Company: Join a forward-thinking tech company with a collaborative and innovative culture.
- Benefits: Competitive salary, health benefits, remote work options, and opportunities for professional growth.
- Other info: Dynamic work environment with excellent career advancement opportunities across major cities.
- Why this job: Make a tangible impact by delivering scalable AI solutions for enterprise clients.
- Qualifications: Experience in machine learning and proficiency in Python and AI frameworks.
The predicted salary is between 60000 - 80000 £ per year.
As an AI/ML Computational Scientist, you will design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients, combining custom models with cloud and third‐party AI services to deliver production‐ready outcomes. Your role spans the full solution lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems.
Responsibilities:
- Formulate real‐world problems into practical, efficient, and scalable AI and Machine Learning solutions.
- Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps.
- Customize and apply Deep Learning and Generative AI models for various use cases based on business needs, data availability, system and infrastructure requirements — including edge devices and HPC.
- Engage in research and development of new AI and high‐performance compute algorithms, models, and simulations along with their applications to solve complex business problems at client sites.
- Work with large‐scale datasets and utilize data preprocessing techniques to ensure high‐quality input for training and production.
- Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools.
- Justify the value of model approaches in business problems.
- Collaborate with teams from both business and technical sides, including users, use‐case representatives, business owners, engineers, architects, and UI designers, to achieve end‐to‐end project goals and integrate into production.
Qualifications:
Education: Bachelor's Degree in Computer Science, Computer Engineering, Data Science, or a related field.
Basic (Required) Qualifications:
- Work or coursework experience with machine learning engineering or machine learning science, deploying models in production at scale, including monitoring, alerting, automatic bug filing, and auditing.
- Experience (work or coursework) in applying theoretical foundations of computer science, including computer system architecture, system engineering, and programming.
Preferred Qualifications:
- Proficiency in Python and Python‐based AI/ML frameworks and familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch).
- Experience working with language models like LLM APIs and optimizing their usage for specific applications.
- Experience with the following programming languages: Python, C++, Java, R, SQL.
- Strong written & verbal communication skills and ability to communicate complex technical concepts to non‐technical stakeholders.
- Strong client‐facing skillsets in a consulting environment.
- Strong cross‐functional skills with the ability to collaborate with a variety of internal and client‐side teams.
- Entrepreneurial mindset with curiosity and passion for emergent tech and driving innovation.
Locations: London, Berlin, Madrid, Paris
AI/ML Junior Computational Scientist in London employer: Accenture
Accenture is an exceptional employer, offering a dynamic work culture in London that fosters innovation and collaboration. With competitive benefits like 30 days of vacation, access to fitness classes, and private medical insurance, employees are supported in achieving a healthy work-life balance while also having ample opportunities for professional growth and development within the rapidly evolving field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land AI/ML Junior Computational Scientist in London
✨Embrace Online Competitions
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We think you need these skills to ace AI/ML Junior Computational Scientist in London
Some tips for your application 🫡
Show Off Your Data Skills:As you're aiming for an entry-level data science role at Accenture, don't forget to highlight your proficiency in programming languages like Python or R. Dive into your CV and mention any relevant projects or coursework that demonstrate your data analysis skills or machine learning knowledge.
Include Relevant Projects:If you've done any data-related projects, whether in your studies or during a personal quest, showcase them in a portfolio. This gives us a tangible sense of your capabilities and shows your hands-on experience with data manipulation, visualisation, or model building.
Tailor Your Cover Letter:When crafting your cover letter, make sure to express your enthusiasm for data science and how this role at Accenture aligns with your career goals. Consider sharing why you’re drawn to data-driven decision-making and how you see yourself growing in this field.
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How to prepare for a job interview at Accenture
✨Brush Up on Your Statistics
For a data science role, the interview may involve some statistical questions or problems. Make sure you're comfortable with concepts like probability, distributions, and hypothesis testing. This will not only help you answer questions but also show your analytical thinking.
✨Get Hands-On with Tools
Familiarise yourself with popular data science tools like Python, R, and SQL. If you're asked about specific projects, be ready to discuss the tools you used and how they contributed to your analysis. Showing that you not only know the theory but can apply it is essential!
✨Showcase Relevant Projects
As an entry-level candidate, your portfolio is crucial. Bring along examples of data projects you've worked on, whether during your studies or personal projects. Discuss the challenges you faced and how you overcame them, highlighting your problem-solving skills.
✨Prepare for Case Studies
Entry-level interviews in data science often include case studies where you'll have to analyse a dataset or solve a problem on the spot. Try out some practice case studies beforehand, so you're not caught off guard. It's all about displaying your thought process and how you tackle data-driven challenges!
