What you'll do :

• Experimenting with range of models, evaluating model performance and model selection.
• Performing data cleaning, feature engineering, selection and evaluation.
• Implementing the data and model training pipelines on cloud using AWS services such as sagemaker, lambda functions, etc.
• Documentation for Model architecture and solutions
• Collaboration with cross-functional teams, including platform engineers, data engineers, software developers and business stakeholders, to ensure solutions meet business needs.
• Adhering to project timelines
• Communicate with non-technical stakeholders to understand their requirements and convey the benefits of the ML solutions

What makes you a great fit :

• 3+ years of experience on Python, SQL, Statistics and Deep Learning
• Exposure to deployments and performance monitoring of statistical and machine learning models in production .
• Experience in object oriented programming and software development lifecycle.
• Exposure on time series Forecasting (elastin) and Prophet time series model.
• Advanced math, probability and statistics knowledge, particularly in the areas of calculus, linear algebra, functional analysis and Bayesian statistics.
• Technical experience implementing, and developing cloud ML models.
• Must have worked on version control systems such as Github and Code commit.
• Exposure of Docker containerization.
• Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks
• Experience developing Predictive modeling and statistics for classification, clustering, forecasting, Time series modeling.
• Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness.
• Experience on Pytorch - TensorFlow.
• Experience with Ensemble machine learning models
• Experience on building solutions on AWS architecture and exposure to AWS services (like Lambda, ECS, Airflow, Elastic search, etc..)
• Experience with AWS services such as Sagemaker for model training, Fine Tuning and Deployment.
• Relevant AWS certifications such as AWS Machine Learning speciality, AWS Solutions Architect.
• Experience with DevOps practices and continuous integration/continuous deployment (CI/CD) pipelines for data solutions.