Data Scientist

At AryaXAI, we’re building the future of explainable, scalable, and aligned AI—designed specifically for high-stakes environments where trust, transparency, and performance are non-negotiable. From financial services to energy and other regulated industries, our platform powers intelligent decision-making through safe and robust AI systems.

We’re looking for a Data Scientist with a deep understanding of both classical and deep learning techniques, experience building enterprise-scale ML pipelines, and the ambition to tackle real-world, high-impact problems. You will work at the intersection of modeling, infrastructure, and regulatory alignment—fine-tuning models that must be auditable, performant, and production-ready.

Responsibilities:

Modeling & AI Development

• Design, build, and fine-tune machine learning models (both classical and deep learning) for complex mission-critical use cases in domains like banking, finance, energy, etc.
• Work on supervised, unsupervised, and semi-supervised learning problems using structured, unstructured, and time-series data.
• Fine-tune foundation models for specialized use cases requiring high interpretability and performance.

Platform Integration

• Develop and deploy models on AryaXAI’s platform to serve real-time or batch inference needs.
• Leverage explainability tools (e.g., DLBacktrace, SHAP, LIME, or AryaXAI’s native xai_evals stack) to ensure transparency and regulatory compliance.
• Design pipelines for data ingestion, transformation, model training, evaluation, and deployment using MLOps best practices.

Enterprise AI Architecture

• Collaborate with product and engineering teams to implement scalable and compliant ML pipelines across cloud and hybrid environments.
• Contribute to designing secure, modular AI workflows that meet enterprise needs—latency, throughput, auditability, and policy constraints.
• Ensure models meet strict regulatory and ethical requirements (e.g., bias mitigation, traceability, explainability).

Requirements:

• 3+ years of experience building ML systems in production, ideally in regulated or enterprise environments.
• Strong proficiency in Python, with experience in libraries like scikit-learn, XGBoost, PyTorch, TensorFlow, or similar.
• Experience with end-to-end model lifecycle: from data preprocessing and feature engineering to deployment and monitoring.
• Deep understanding of enterprise ML architecture—model versioning, reproducibility, CI/CD for ML, and governance.
• Experience working with regulatory, audit, or safety constraints in data science or ML systems.
• Familiarity with ML Ops tools (MLflow, SageMaker, Vertex AI, etc.) and cloud platforms (AWS, Azure, GCP).
• Strong communication skills and an ability to translate technical outcomes into business impact.

Bonus Points For

• Prior experience in regulated industries: banking, insurance, energy, or critical infrastructure.
• Experience with time-series modeling, anomaly detection, underwriting, fraud detection or risk scoring systems.
• Knowledge of RAG architectures, generative AI, or foundation model fine-tuning.
• Exposure to privacy-preserving ML, model monitoring, and bias mitigation frameworks.

What You’ll Get

• Competitive compensation with performance-based upside
• Comprehensive health coverage for you and your family
• Opportunity to work on mission-critical AI systems where your models drive real-world decisions
• Ownership of core components in a platform used by top-tier enterprises
• Career growth in a fast-paced, high-impact startup environment
• Remote-first, collaborative, and high-performance team culture

If you’re excited to build data science solutions that truly matter, especially in the most demanding industries, we want to hear from you.