Lead Data Scientist

Role & responsibilities

• Lead AI/ML solution development for aerospace and industrial technology initiatives.
• Architect scalable deep learning models (CNNs, Transformers, LLMs, Vision-Language Models).
• Research and apply Machine Learning & Deep Learning for engineering simulations and predictive analytics.
• Spearhead the integration of Generative AI technologies, including building RAG pipelines and domain-specific LLMs.
• Collaborate closely with engineering, digital, and business stakeholders to identify opportunities and deliver impactful AI solutions.
• Mentor junior data scientists and guide technical solution architecture in project teams.
• Manage project timelines, technical risks, and ensure deliverables meet quality and performance standards.
• Stay abreast of the latest AI research and translate advances into project opportunities.
• Expertise in LLMOps
• Experience working with diverse and complex datasets, including time-series sensor data, simulation outputs, engineering text/diagrams, or geospatial data, is a significant advantage

Preferred candidate profile

• Masters in Statistics, Data Science, AI, Applied Mathematics, or related fields.
• 8+ years of strong industry experience developing and deploying AI/ML systems.
• Expertise in Deep Learning, including CNNs, Transformers, and modern architectures.
• Hands-on experience with PINNs and physics-informed modeling approaches.
• Proficiency with GenAI toolkits and techniques (e.g., Hugging Face, LangChain, LlamaIndex).
• Strong coding expertise in Python, with familiarity across the ML ecosystem (TensorFlow, PyTorch, Hugging Face, Scikit-learn).
• Good knowledge of MLOps, containerization, and cloud/on-premise AI deployment practices.
• Proven track record of leading technical teams in delivering successful AI projects.
• Strong communication skills, ability to work independently, and an innovation-driven mindset.