Principal Data Scientist - Applied Research & Deep Learning

Role Overview: You are a highly experienced Senior/Principal Architect (Scientist) with expertise in Applied Mathematics, Statistical Modeling, and Deep Learning. Your primary responsibility will be to lead the design, implementation, and delivery of next-generation AI models and algorithms. Operating at the intersection of business problem-solving, mathematical modeling, and software development, you will translate complex real-world problems into formal mathematical and statistical representations, design and implement advanced deep learning and Agentic systems, and ensure their reliable deployment in production environments. Key Responsibilities: - Own the end-to-end mathematical and statistical architecture of complex AI systems, covering data integration, feature engineering, model training, inference, and monitoring. - Design scalable, robust, and secure AI models and algorithms for large-scale enterprise systems, based on first-principles mathematical design. - Establish architectural standards, design principles, and best practices for AI-centric systems organization-wide. - Translate business and domain problems into mathematical, statistical, and agentic models. - Apply foundational techniques from linear algebra, optimization, numerical methods, statistics, and causal inference to enhance model robustness and performance. - Design, develop, and deploy advanced deep learning models such as transformers, state-space, and diffusion models. - Optimize models and pipelines for speed, scalability, memory/compute efficiency, and accuracy using techniques like pruning, quantization, distributed training, and GPU/accelerator optimization. - Act as a technical mentor and thought leader for AI engineers, AI scientists, and software developers, guiding them on architecture, quality, and maintainability. - Evaluate, select, and evolve AI frameworks, libraries, tools, and cloud platforms to drive innovation and address business challenges. Qualifications Required: - Education: Masters or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a closely related quantitative discipline. - Professional Experience: 10+ years of overall software engineering experience, 5+ years of hands-on experience in applied mathematics, statistical modeling, and deep learning systems, with demonstrated experience in architecting and deploying AI solutions in production environments. - Deep Learning Expertise: Strong experience in designing, training, and deploying deep learning models using modern AI frameworks, understanding the full ML lifecycle, working with large datasets, and distributed or high-performance computing environments. - Mathematics & Statistics: Deep expertise in linear algebra, multivariate calculus, probability theory, optimization, statistical modeling, and causal inference, with the ability to reason formally about model behavior and performance trade-offs. - Programming & Engineering Skills: Expert-level proficiency in Python, including mathematical, statistical, ML, and agentic libraries, strong software engineering fundamentals, and experience with additional languages like Scala, Kotlin, or Rust is desirable. (Note: Omitting additional details of the company as they were not provided in the job description) Role Overview: You are a highly experienced Senior/Principal Architect (Scientist) with expertise in Applied Mathematics, Statistical Modeling, and Deep Learning. Your primary responsibility will be to lead the design, implementation, and delivery of next-generation AI models and algorithms. Operating at the intersection of business problem-solving, mathematical modeling, and software development, you will translate complex real-world problems into formal mathematical and statistical representations, design and implement advanced deep learning and Agentic systems, and ensure their reliable deployment in production environments. Key Responsibilities: - Own the end-to-end mathematical and statistical architecture of complex AI systems, covering data integration, feature engineering, model training, inference, and monitoring. - Design scalable, robust, and secure AI models and algorithms for large-scale enterprise systems, based on first-principles mathematical design. - Establish architectural standards, design principles, and best practices for AI-centric systems organization-wide. - Translate business and domain problems into mathematical, statistical, and agentic models. - Apply foundational techniques from linear algebra, optimization, numerical methods, statistics, and causal inference to enhance model robustness and performance. - Design, develop, and deploy advanced deep learning models such as transformers, state-space, and diffusion models. - Optimize models and pipelines for speed, scalability, memory/compute efficiency, and accuracy using techniques like pruning, quantization, distributed training, and GPU/accelerator optimization. - Act as a technical mentor and thought l