Data Scientist - AWS

Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers. BE/BS in Computer Science, Math, Physics, or other technical fields. DataScience Hands on experience and strong knowledge of building machine learning models supervised and unsupervised models Programming Skills Hands-on experience in statistical programming languages likePythonand database query languages likeSQL Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pigis an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers. BE/BS in Computer Science, Math, Physics, or other technical fields. DataScience Hands on experience and strong knowledge of building machine learning models supervised and unsupervised models Programming Skills Hands-on experience in statistical programming languages likePythonand database query languages likeSQL Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pigis an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills