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- Tasks: Lead the design and development of LLM-based solutions to enhance product features.
- Company: Join Checkout.com, a top fintech empowering businesses in the digital economy.
- Benefits: Enjoy a collaborative culture, flexible work options, and opportunities for personal growth.
- Why this job: Be at the forefront of AI innovation while making a real impact on global enterprises.
- Qualifications: Experience with LLMs, Python, and strong analytical skills are essential.
- Other info: We celebrate diversity and support your success in a welcoming environment.
The predicted salary is between 36000 - 60000 £ per year.
Company Description
Checkout.com is one of the most exciting fintechs in the world. Our mission is to enable businesses and their communities to thrive in the digital economy. We\’re the strategic payments partner for some of the best known fast-moving brands globally such as Wise, Hut Group, Sony Electronics, Homebase, Henkel, Klarna and many others. Purpose-built with performance and scalability in mind, our flexible cloud-based payments platform helps global enterprises launch new products and create experiences customers love. And it\’s not just what we build that makes us different. It\’s how.
We empower passionate problem-solvers to collaborate, innovate and do their best work. That\’s why we\’re on the Forbes Cloud 100 list and a Great Place to Work accredited company. And we\’re just getting started. We\’re building diverse and inclusive teams around the world – because that\’s how we create even better experiences for our merchants and our partners. And we need your help. Join us to build the digital economy of tomorrow.
Job Description
As a Staff Data Scientist specialising in Large Language Models (LLMs), you will play a critical role in harnessing the power of advanced NLP technologies to drive innovation and efficiency across our enterprise. As part of Checkout.com\’s AI centre of excellence, you will lead LLM-based solutions\’ design, development, and deployment, collaborating with cross-functional teams to deliver impactful AI-driven applications.
How you\’ll make an impact:
- In collaboration with product managers and engineers, research, scope and validate use cases where LLMs can improve Checkout.com\’s product features and business processes.
- Design, develop, and fine-tune LLMs for various applications such as chatbots, virtual assistants, text generation, and more.
- Ensure we have the right processes and tools to curate and preprocess large datasets for training and evaluating LLMs, implement strategies for data augmentation, labeling, and annotation.
- As the technical thought leader, increase the AI fluency in the wider business through supporting training programs and mentoring others.
- Ensure that LLM applications adhere to ethical standards and comply with relevant regulations.
Qualifications
- Proven track record of developing and deploying LLM-based solutions in an enterprise setting as a senior/staff scientist.
- Proficiency in Python and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and spaCy. Strong understanding of LLM architectures (e.g., GPT, BERT, T5) and experience fine-tuning them for specific tasks.
- Demonstrable experience in utilising different model architectures and training techniques to optimize performance.
- Familiarity with prompt engineering techniques and frameworks like LangChain, LlamaIndex, or DSpy. Good understanding of LLM models, including other components like VectorDBs and document loaders.
- Strong analytical and problem-solving skills, with the ability to work with complex datasets and extract meaningful insights.
- Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Strong ability to collaborate and communicate with a large and varied group of stakeholders to embed AI into workflows and product features.
Nice to have:
- Experience with conversational AI and chatbot development.
- Familiarity with ethical considerations and best practices in AI.
- Previous experience in a mentorship or leadership role within a data science team.
Additional Information
We believe in equal opportunities
We work as one team. Wherever you come from. However you identify. And whichever payment method you use.
Our clients come from all over the world – and so do we. Hiring hard-working people and giving them a community to thrive in is critical to our success.
When you join our team, we\’ll empower you to unlock your potential so you can do your best work. We\’d love to hear how you think you could make a difference here with us.
We want to set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable. We\’ll be happy to support you.
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Contact Detail:
Checkout.com Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Data Scientist, Applied AI
✨Tip Number 1
Familiarise yourself with the latest advancements in Large Language Models (LLMs) and their applications in fintech. This will not only help you understand the role better but also allow you to discuss relevant use cases during interviews.
✨Tip Number 2
Engage with the data science community by attending webinars, workshops, or meetups focused on NLP and LLMs. Networking with professionals in the field can provide insights and potentially lead to referrals.
✨Tip Number 3
Prepare to showcase your experience with Python and relevant libraries like TensorFlow and PyTorch. Be ready to discuss specific projects where you've developed or deployed LLM-based solutions, as practical examples can set you apart.
✨Tip Number 4
Highlight your ability to communicate complex technical concepts to non-technical stakeholders. Practice explaining your past projects in simple terms, as this skill is crucial for collaboration within cross-functional teams at Checkout.com.
We think you need these skills to ace Staff Data Scientist, Applied AI
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and qualifications for the Staff Data Scientist position. Familiarise yourself with LLMs and their applications in fintech to tailor your application effectively.
Highlight Relevant Experience: In your CV and cover letter, emphasise your proven track record in developing and deploying LLM-based solutions. Include specific examples of projects where you've used Python and relevant libraries like TensorFlow or PyTorch.
Showcase Your Skills: Clearly outline your proficiency in LLM architectures and any experience with prompt engineering techniques. Mention your analytical skills and ability to work with complex datasets, as these are crucial for the role.
Communicate Effectively: Since excellent communication skills are essential, ensure your application clearly conveys your ability to explain complex technical concepts to non-technical stakeholders. Use straightforward language and avoid jargon where possible.
How to prepare for a job interview at Checkout.com
✨Showcase Your LLM Expertise
Be prepared to discuss your experience with large language models in detail. Highlight specific projects where you've developed or deployed LLM-based solutions, and be ready to explain the architectures you used, such as GPT or BERT.
✨Demonstrate Problem-Solving Skills
Prepare examples of how you've tackled complex datasets and extracted meaningful insights. Discuss your analytical approach and any innovative solutions you've implemented in previous roles.
✨Communicate Clearly with Non-Technical Stakeholders
Since you'll need to explain complex technical concepts to a varied audience, practice simplifying your explanations. Use analogies or relatable examples to convey your ideas effectively.
✨Emphasise Collaboration and Mentorship
Highlight your experience working in cross-functional teams and any mentorship roles you've held. Showcase how you've helped others increase their AI fluency and contributed to a collaborative work environment.
