Data Scientist

AryaXAI

Data Scientist

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

matter