Youll lead projects from early research to full production deployment, guiding the development of models, scalable pipelines, and services that power critical features in the platform.
Responsibilities
Lead, mentor, and grow a team of Data Scientists providing both technical leadership and professional development.
Own the full lifecycle of AI projects: from research and prototyping through production-grade deployment and monitoring.
Design and oversee the development of scalable ML pipelines, model serving infrastructure, and robust data workflows in collaboration with software engineering and product teams.
Drive initiatives to deliver LLMs and other AI models as production-grade services within the organization.
Partner cross-functionally with product managers, clinical experts, and engineers to translate business needs into AI-driven features.
Guide research in NLP, NLU, and Deep Learning domains, staying at the forefront of industry developments and incorporating new methods where applicable.
Maintain a high standard of technical execution within the team, fostering a culture of ownership, quality, and innovation.
Communicate technical findings, project status, and strategic recommendations clearly to technical and non-technical stakeholders.
Requirements:
B.Sc and M.Sc in Computer Science, Engineering, Bioinformatics, or a related analytical field.
4+ years of hands-on experience with Machine Learning, Deep Learning, and NLP algorithms, with at least 2+ years in a leadership or technical mentorship role.
High proficiency in Python, with a strong foundation in software engineering best practices.
Demonstrated experience building and deploying end-to-end data science solutions at scale, including model serving, API development, and monitoring.
Solid understanding of NLP methodologies such as language modeling, embeddings, Question Answering, Reading Comprehension, etc., or equivalent experience in related Deep Learning domains.
Hands-on experience designing scalable ML pipelines and model deployment architectures (batch and real-time).
Team player with excellent communication skills and a passion for mentoring others.
Advantages:
Ph.D. in Computer Science, Engineering, or a related field.
Experience working with cloud environments and infrastructure (AWS, Docker, Kubernetes).
Experience developing and deploying LLM-based solutions in production environments.
Familiarity with MLOps best practices and tools (e.g., SageMaker, Annotation tools, Experiment management tools).
