Job Description

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Job Description –
Google Cloud Platform (GCP) Applied ML Engineer: An expert engineer with an eye for AI. You want to change how the world works and lives by taking AI out of the lab and into everyday life. You are primarily a software engineer with a very strong interest in data science (not the other way around i.e. this is not a data scientits role). As a machine learning engineer, you’ll be required to develop a more holistic understanding of the software you are building, including transferring some of the software engineering priciples to the data science world. Having a deep understanding of the mathematical underpinnings of the Machine Learning algorithms is a big plus, but it’s NOT necessary. However, you’ll be required to know what algorithms are available —or when and how to apply them. Machine Learning output is actually working software!
Role & Responsibilities–
• "Use deep learning and machine learning to solve business problems
• Run deep learning and machine learning projects from beginning to end
• Work on projects that cover everything from business understanding to model building, validation, and deployment
• Use Accenture tools and techniques
• Report on your progress regularly in writing and face-to-face
• Stay up-to-date on new products that clients could use
• Tweak existing methods and procedures to create solutions to moderately complex problems
• Use your judgment to craft solutions to complex problems or seek guidance as needed
• Prototype and demonstrate products for clients in customer environments.
• Work with peers and senior colleagues in Accenture and at client companies
• Help the sales team identify and classify opportunities
• Find out where clients need help and produce “as is” and “to be” scenarios
• Implement management’s strategy"
• Integrate massive datasets from multiple data sources for data modelling
• Implement methods for automation of all parts of the predictive pipeline to minimize labour in development and production
• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product management
• Knowledge in machine learning algorithms especially in recommender systems
• Extracting, Loading, Transforming, cleaning, and validating dat
• Designing pipelines and architectures for data processing
• Creating and maintaining machine learning and statistical models
• Querying datasets, visualizing query results and creating reports
• Extensive travel may be required
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Skills - Overview Basic Qualifications – • "Minimum 1 to 6 years of experience in data science and use of statistical methodologies • Minimum 1-3 years of developing machine learning methods, including familiarity with techniques in clustering, regression, optimization, recommendation, neural networks, and other. • Strong quantitative and analytical skills with minimum 2 years of experience with data science tools, including Python, R, Scala, Julia, or SAS • Bachelor’s degree in data science and related disciplines as mathematics, statistics, computer science, physics, or related fields. Master’s degree preferred" • Minimum 1 year of implementing and delivering projects using CI/CD rigor and tools such as git, Jenkins, docker, Kubernates, Kubeflow Pipelines etc. • Minimum 1 year of architecting and implementing next generation data and analytics platforms on GCP cloud • Minimum 1 year of designing and implementing data engineering, ingestion and curation functions on GCP cloud using GCP native or custom programming • Minimum 1 year of experience in performing detail assessments of current state data platforms and creating an appropriate transition path to GCP cloud • Hands-on GCP experience with a minimum of 1 solutions designed and implemented at production scale Preferred Skills – • Minimum 1 year of experience in architecting large-scale data solutions, performing architectural assessments, crafting architectur

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