Lead Data Scientist

As a Lead Data Scientist for multilingual Artificial Intelligence and Machine Learning initiatives, your main focus will be on advanced natural language processing, cutting-edge speech technologies, and developing highly specialized AI/ML models tailored to specific domains. You will be responsible for leading the end-to-end development of multilingual transformer models, conducting advanced fine-tuning of speech-to-text transcription models, and adapting large language models for specialized applications, particularly within the fintech sector. Your role will involve architecting and building complex conversational AI systems, designing synthetic data pipelines, and implementing cross-lingual training strategies to enhance model performance across multiple languages. In addition to technical leadership, you will mentor and guide junior and mid-level data scientists and ML engineers, ensuring a culture of technical excellence, continuous learning, and best practices in AI/ML development. Collaboration with engineering teams will be essential to ensure efficient and scalable model deployment, monitoring, and adherence to MLOps practices in production environments. Staying updated with the latest research and advancements in multilingual NLP, speech AI, and LLM technologies will be crucial for integrating innovative approaches into the product strategy. The ideal candidate for this role should hold a Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Linguistics, Statistics, or a related quantitative field. You should have at least 5 years of progressive experience in Machine Learning and Natural Language Processing, with a strong foundation in Python, deep learning frameworks like PyTorch and TensorFlow, and the Hugging Face ecosystem. Experience in MLOps practices, containerization technologies, cloud platforms, and data privacy regulations is also required. Strong analytical, problem-solving, and communication skills are essential for effectively articulating complex technical concepts to diverse stakeholders. Preferred skills for this role include experience with other multilingual NLP toolkits, distributed training of large models, publications in relevant AI/ML conferences or journals, familiarity with other speech AI frameworks, and knowledge of graph databases or knowledge graph construction.,