Staff ML Scientist - Pfi

Visa Visa · Fintech · Bengaluru, India, IN

Staff ML Scientist at Visa focused on building, training, testing, validating, and productizing machine learning models. The role involves implementing MLOps best practices, utilizing AWS SageMaker, and understanding model explainability. Experience with deep learning frameworks and model serving is required, with a focus on fintech applications.

What you'd actually do

  1. Hands‑on experience building, training, testing, validating, and productizing machine learning models for high‑performance use cases.
  2. Experience implementing MLOps best practices, including model versioning, monitoring, and CI/CD pipelines for ML models.
  3. Hands‑on experience with AWS SageMaker for building, training, tuning, and deploying ML models.
  4. Strong understanding of model explainability frameworks such as SHAP, and the ability to interpret and explain model behavior.
  5. Experience debugging and analyzing false positive and false negative cases, including supporting client or production issues.

Skills

Required

  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • scikit-learn
  • machine learning concepts
  • feature engineering
  • model evaluation
  • model optimization
  • MLOps
  • AWS SageMaker
  • AWS services (S3, EC2, ECR, EKS, Lambda, CloudWatch)
  • model explainability (SHAP)
  • debugging ML models
  • deep learning models
  • TensorFlow
  • PyTorch
  • Keras
  • problem-solving
  • ML lifecycle management
  • experimentation frameworks (MLflow)

Nice to have

  • model serving and inference engines (TensorFlow Serving, Triton Inference Server)
  • Spark-based data and feature pipelines
  • big data platforms (Hadoop, EMR)
  • NoSQL databases

What the JD emphasized

  • productizing machine learning models
  • MLOps best practices
  • AWS SageMaker
  • model explainability frameworks
  • deep learning models
  • model serving and inference engines

Other signals

  • productizing machine learning models
  • MLOps best practices
  • AWS SageMaker
  • model explainability frameworks
  • deep learning models
  • model serving and inference engines