Staff ML Scientist - PFI

Essential Functions:

• Proficient in exploratory data analysis (EDA) using Pythons scientific libraries including NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn.
• Strong development experience in Python.
• Hands-on experience building, training, testing, validating, and productizing machine learning models for high-performance use cases.
• Solid understanding of core machine learning concepts, including feature engineering, model evaluation, and optimization.
• Experience implementing MLOps best practices, including model versioning, monitoring, and CI/CD pipelines for ML models.
• Hands-on experience with AWS SageMaker for building, training, tuning, and deploying ML models.
• Hands-on experience with to AWS services for machine learning workloads, such as S3, EC2, ECR, EKS, Lambda, CloudWatch.
• Strong understanding of model explainability frameworks such as SHAP, and the ability to interpret and explain model behavior.
• Experience debugging and analyzing false positive and false negative cases, including supporting client or production issues.
• Hands-on experience and solid understanding of deep learning models, with exposure to frameworks such as TensorFlow, PyTorch, or Keras.
• Strong problem-solving skills with the ability to move beyond tasks and propose improved or alternative solutions.
• Experience with ML lifecycle management and experimentation frameworks such as MLflow

Qualifications

• 8+ yrs
• work experience with a Bachelors Degree or 6+ years of work experience with a Master's or Advanced Degree in an analytical field such as computer science, statistics, finance, economics or relevant area
• Additional Skills (Plus)Exposure to model serving and inference engines such as TensorFlow Serving, Triton Inference Server, or similar technologies
• Experience building and maintaining Spark-based data and feature pipelines to support ML training and inference workflows
• Familiarity with big data platforms and storage systems such as Hadoop, EMR, and NoSQL databases