Sr Data Scientist (London)

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly.

Our mission is to create AI tools that empower researchers, engineers, and organizations - including banks, financial institutions, and large enterprises - to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance.

Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, every team member contributes hands on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that our work not only advances technology but also meets the rigorous demands of regulated sectors.

Role Overview

As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You'll lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors.

Responsibilities

• Model Evaluation & Customization: Evaluate, fine tune, and implement appropriate AI/ML models on tailored for enterprise and regulated use cases, considering factors such as accuracy, computational efficiency, scalability, and regulatory constraints.
• Architectural Assessment: Assess and recommend model architectures that meet the high standards required by complex business problems in financial services and other regulated industries.
• Enterprise Integration: Lead the deployment of AI models into production environments, ensuring seamless integration with existing enterprise systems while upholding strict compliance and security standards.
• Advanced AI Techniques: Drive the development and implementation of state of the art AI architectures, incorporating advanced explainability, AI safety, and alignment techniques suited for regulated applications.
• Specialization & Innovation: Take ownership of specialized areas within machine learning or deep learning to address challenges related to complex datasets, regulatory requirements, and enterprise-grade AI solutions.
• Collaboration & Quality Assurance: Collaborate closely with Machine Learning Engineers and Software Development Engineers to roll out features, manage quality assurance, and ensure all deployed models meet performance and compliance benchmarks.
• Documentation & Compliance: Create and maintain detailed technical and product documentation with an emphasis on auditability and adherence to regulatory standards.

Qualifications

• Educational & Professional Background: A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors.
• Regulated industry experience (financial services, banking, or insurance preferred).
• A proven track record (2+ years) of hands on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings.
• Technical Expertise: Demonstrated proficiency with deep learning frameworks such as TensorFlow or PyTorch, and experience implementing advanced techniques such as transformer models or GANs.
• Diverse Data Handling: Experience working with varied data types - including textual, tabular, categorical, and image data - and the ability to develop models for complex enterprise level datasets.
• Expertise in deploying AI solutions in cloud and on premise environments, ensuring robust, scalable, and secure integrations with enterprise systems.
• Publications & Contributions: Peer reviewed publications or significant contributions to open source AI tools are highly regarded.