Sr. ML scientist

• Proficient in exploratory data analysis (EDA) using Pythons scientific libraries including numpy, pandas, matplotlib, seaborn, and scikit-learn
• Exposure to model development frameworks like MLFlow
• Experience using Papermill for parameterizing and executing Jupyter Notebooks
• Strong development experience in at least one of the following: Python, R (preferably Python)
• Implementation of MLOps practices including continuous integration and deployment (CI/CD) for ML models
• Hands on experience in building and maintaining data pipelines, feature engineering pipelines and comfortable with core ML concepts.
• Hands-on experience in engineering, testing, validating, and productizing ML models for high-performance use cases
• Hands-on experience with AWS Sagemaker for building, training, and deploying ML models
• Develop and implement practices for deploying machine learning models in large data science projects
• Proven experience in building and training complex ML models
• Experience using and maintaining DevOps tools and implementing automations for production
• Additional knowledge of AWS services and ecosystems
• Experience working with containerized and virtualized environments (Docker, K8s)

Basic Qualification
-4+ yrs. work experience with a Bachelor s Degree or 2+ years of work experience with a Masters or Advanced Degree in an analytical field such as computer science, statistics, finance, economics or relevant area.

Technical skills:
-Proficient in exploratory data analysis (EDA) using Pythons scientific libraries including numpy, pandas, matplotlib, seaborn, and scikit-learn
-Exposure to frameworks like MLflow for model lifecycle management
-Strong development experience in programming languages, preferably Python
-Implementation of MLOps practices, including continuous integration and deployment (CI/CD) for ML models.
-Experience with complex, high-volume, multi-dimensional data, as well as machine learning models based on unstructured, structured, and streaming datasets.
-Experience with Unix/Shell or Python scripting and exposure to scheduling tools like Oozie and Airflow.
-Hands-on experience with AWS SageMaker for building, training, and deploying ML models.
-Proven experience in building and training complex ML models.
-Experience using Papermill for parameterizing and executing Jupyter Notebooks.
-Experience with SQL for extracting, aggregating, and processing big data pipelines using Hadoop, EMR, and NoSQL Databases.
-Exposure to model serving engines such as Tensorflow, Triton etc.
-Spark Pipelines: Build and maintain efficient and robust Spark pipelines to create and access data sets and feature stores for ML models.
-ETL processes: The role also involves developing and executing large scale ETL processes to support data quality, reporting, data marts, and predictive modeling.
-Knowledge of standard Big data and Real Time stack such as Hadoop, Spark, Kafka, Redis, Flink and similar technologies