Senior/Lead Data Scientist (LLMs)

As a Senior/Lead Data Scientist (LLMs) at Capco, your key responsibilities include leading the research and development of LLM architectures, fine-tuning pre-trained models for specific tasks, and collaborating closely with cross-functional teams. Additionally, you will mentor junior data scientists, enhance model training processes, and ensure the ethical deployment of AI models across our projects.

To qualify for the Senior/Lead Data Scientist (LLMs) position at Capco, you should hold a Bachelor's, Master's, or Ph.D. in Computer Science, AI, Data Science, or a related field. Furthermore, you will need at least 5 years of experience working with LLMs and NLP in production settings, preferably in the financial services sector. Proficiency in Python and experience with frameworks like TensorFlow and PyTorch are also essential.

At Capco, a Senior/Lead Data Scientist (LLMs) will work on diverse projects involving the development of large language models for applications such as text generation and question-answering systems. You'll also engage with innovative data practices, integrating scalable AI solutions into banking, insurance, and asset management systems, thus making a significant impact in the financial industry.

Capco emphasizes ethical AI principles in all its operations. As a Senior/Lead Data Scientist (LLMs), you will be responsible for ensuring that all AI models comply with ethical standards and security requirements while working on cutting-edge projects. This includes maintaining data privacy and governance standards to safeguard client information.

Essential tools for a Senior/Lead Data Scientist (LLMs) at Capco include Python programming, and experience with libraries like TensorFlow, PyTorch, and Hugging Face Transformers is crucial. Familiarity with cloud platforms such as AWS, Azure, or GCP for deploying models will also greatly enhance your effectiveness in this role.

When discussing your experience with large language models, focus on specific projects where you designed or implemented LLM architectures. Highlight the outcomes, such as improvements in text generation or summarization tasks, and explain the methodologies you used to fine-tune and optimize these models for targeted applications.

In your response, emphasize your systematic approach to data preprocessing. Talk about steps like cleaning, annotating, and validating datasets. Share techniques you use to identify data quality issues and how you leverage tools to automate parts of this process, enhancing efficiency in preparing data for model training.

Sharing a specific incident where you successfully mentored a junior data scientist will showcase your leadership skills. Briefly outline the challenges they faced and how you guided them, emphasizing your ability to communicate complex concepts clearly and help them implement best practices effectively.

Discuss your strategies, such as following key journals, participating in webinars, or attending conferences. Mention any influential researchers or thought leaders whose work you follow, and explain how you apply new findings to enhance your projects, showcasing your commitment to continuous learning.

Reference specific deployment projects, discussing the challenges you encountered and how you overcame them. Focus on the tools and cloud platforms you utilized for deployment and the importance of monitoring and maintaining model performance post-deployment.

In your answer, outline various model evaluation metrics you employ, such as accuracy, precision, recall, and F1 score. Describe the processes for validating your models, including cross-validation techniques and how those metrics influence your model selection and optimization.

Discuss your understanding of ethical principles in AI, such as fairness, accountability, and transparency. Give examples of how you monitor AI models for biases and ensure they meet compliance standards throughout their development and deployment lifecycle.

Talk about your direct experience working with cloud services like AWS, Azure, or GCP for deploying machine learning models. Highlight specific tools or frameworks you have used provided by these platforms and how they have enhanced scalability and model maintenance.

Identify specific challenges such as handling unstructured data or implementing complex algorithms. Discuss how you navigated these challenges effectively and what lessons you learned that have helped you improve your approach in subsequent projects.

Share your strategies for fostering collaboration, such as regular sync-up meetings, using collaborative platforms, and encouraging an open culture for sharing ideas. Emphasize the importance of diversity in perspectives for successfully integrating models into applications.