Data Science Lead

• Master’s degree or higher in Data Science, Computer Science, Statistics, or a closely related field. A doctoral degree (PhD) is highly desirable, particularly if it focuses on AI, machine learning, or computational statistics, which will demonstrate deep theoretical knowledge and research capabilities.
• Working experience in field of Generative AI, Machine learning or NLP, ideally within the academic research or financial sector, encompassing the development and operational deployment of machine learning algorithms.
• A proven track record of impactful research with publications in respected journals and significant contributions to major projects, particularly those involving advanced AI technologies
  

Knowledge And Skills

• Profound mastery in applying cutting-edge AI techniques, including but not limited to machine learning, deep learning (e.g., CNNs, RNNs, GANs), and neural networks. Expertise in the design and tuning of transformer models specifically for complex NLP tasks such as sentiment analysis, named entity recognition, and machine translation
• Demonstrated ability in developing robust algorithms using state-of-the-art methods such as Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and advanced ensemble techniques. Experience in optimizing algorithms for performance and scalability, with a focus on reducing computational costs and improving model efficiency.
• Advanced knowledge of data architecture principles, capable of designing and overseeing the implementation of robust data pipelines for AI applications. Proficiency in managing both structured and unstructured data, applying best practices in data warehousing, ETL processes, and real-time data streaming (e.g., Kafka, Storm).
• Expertise in deploying AI models and managing infrastructures using cloud services such as AWS, Azure, and Google Cloud Platform. Familiarity with DevOps practices for AI, including continuous integration and continuous deployment (CI/CD) pipelines, containerization with Docker, and orchestration with Kubernetes.
• Expert-level proficiency in natural language processing, understanding both traditional linguistic approaches and modern deep learning-based methods. Ability to leverage libraries such as NLTK, spaCy, and Hugging Face’s Transformers for advanced text analysis and processing