Principal Data Scientist - Applied Research & Deep Learning

Role: Senior Architect - Applied Research & Deep Learning

Role Summary:

We are seeking a highly experienced, intellectually curious Senior/Principal Architect (Scientist) with a strong foundation in Applied Mathematics, Statistical Modeling, and Deep Learning to lead the design, implementation and delivery of next-generation AI models and algorithms.

In this role, you will operate at the intersection of business problem-solving, mathematical modeling, software development and Agentic AI 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.

The ideal candidate combines software engineering, statistical modeling, AI techniques, and a strong passion for applied mathematics and innovation, with the ability to influence technical direction across teams.

What You Will Do

Architectural Leadership

Own the end-to-end mathematical and statistical architecture of complex AI systems, from data integration and feature engineering to model training, inference, and monitoring.

Design scalable, robust and secure AI models and algorithms capable of handling large-scale, complex enterprise systems, grounded in first-principles mathematical design.

Establish architectural standards, design principles, and best practices for AI-centric systems across the organization.

Applied Mathematics & Statistical Modeling

Translate business and domain problems into mathematical, statistical, and agentic models.

Apply foundational techniques from linear algebra, optimization, numerical methods, statistics, causal inference to improve model robustness and performance.

Guide teams in selecting appropriate modeling approaches, loss functions, evaluation metrics, and validation strategies.

Deep Learning

Design, develop, and deploy advanced deep learning models (e.g., transformers, state-space, diffusion models)

Oversee the full model lifecycle, including experimentation, training, evaluation, deployment, and continuous improvement.

Ensure models are production-ready, explainable where required, and aligned with business objectives.

Optimize models and pipelines for speed, scalability, memory/compute efficiency, and accuracy.

Apply techniques such as pruning, quantization, distributed training, and GPU/accelerator optimization.

Collaborate with platform and infrastructure teams to maximize system-level performance.

Technical Mentorship & Influence

Act as a technical mentor and thought leader for AI engineers, AI scientists and software developers.

Review designs, code, and models, providing guidance on architecture, quality, and maintainability.

Elevate modeling maturity by promoting best practices in model design, algorithm development, coding, testing, and documentation.

Strategy, Research & Innovation

Evaluate, select, and evolve AI frameworks, libraries, tools, and cloud platforms.

Stay current with cutting-edge research, industry trends, and emerging technologies in AI and applied mathematics.

Drive innovation by identifying opportunities to apply advanced AI techniques to business challenges.

What Youll have

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.

Demonstrated experience architecting and deploying AI solutions in production environments.

Deep Learning Expertise

Strong experience designing, training, and deploying deep learning models using modern AI frameworks.

Solid understanding of the full ML lifecycle, including experimentation, deployment, monitoring, and retraining.

Experience 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.

Ability to reason formally about model behavior, convergence, and performance trade-offs.

Programming & Engineering Skills

Expert-level proficiency in Python, including state-of-the-art mathematical, statistical, ML and agentic libraries

Strong software engineering fundamentals: data structures, algorithms, system design, and performance optimization.

Experience with additional languages such as Scala, Kotlin or Rust is highly desirable.

What well do for you

• Competitive salary with stock options to eligible candidates
• Flat organization: With a very strong entrepreneurial culture (and no corporate politics)
• Great people and unlimited fun at work
• Possibility to make a difference in a scale-up environment.
• Opportunity to travel onsite in specific phases depending on project requirements.
• Support network: Work with a team you can learn from everyday.
• Diversity: We pride ourselves on our international working environment.
• DavOs Insight (Turning VUCA into value with Neuro-Symbolic AI: https://l1nk.dev/ngQoe
• Work-Life Balance: https://youtu.be/IHSZeUPATBAfeature=shared
• Feel part of A team: https://youtu.be/QbjtgaCyhesfeature=shared

How the process works...

• Setup & Create your profile in Workday to track the status - Link to apply. If already done, kindly ignore!
• Respond with your interest to us.
• Well contact you either via video call or phone call - whatever you prefer, with the further schedule status.
• During the interview phase, you will meet with Executives from the Leadership team and the technical panel for 60 minutes. We will contact you after the interview to let you know if wed like to progress your application.
• There will be a Introductory call with the EVP, 2 rounds of technical discussion with the VPs and Architects followed by a Techno Managerial round.
• We will let you know if youre the successful candidate.

Good luck!