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 AryaXAI.com tailored for enterprise and regulated use cases. Consider factors such as accuracy, computational efficiency, scalability, and regulatory constraints.
- Architectural Assessment: Assess and recommend various model architectures, ensuring that selected solutions 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/deep learning to address specific challenges related to complex datasets, regulatory requirements, and enterprise‑grade AI solutions.
- Collaboration & Quality Assurance: Collaborate closely with Machine Learning Engineers (MLEs) and Software Development Engineers (SDEs) to rollout features, manage quality assurance, and ensure that all deployed models meet both 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 (FS, Banking or Insurance is preferred): 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 (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.).
- Diverse Data Handling: Experience working with varied data types—including textual, tabular, categorical, and image data—and the ability to develop models that handle complex, enterprise‑level datasets.
- Deployment Proficiency: Expertise in deploying AI solutions in both 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 tools in AI are highly regarded.
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