Data Scientist-Tech Con-AI and Data-AI and Quan Modelling-Azure Data Scientist-Mgr-Mult Pos-1680332

EY•San Jose, CA

1d•$162,615

About The Position

EY focuses on high-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities. At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.Data Scientist - Technology Consulting - AI and Data - AI and Quantitative Modelling - Azure Data Scientist - Manager - Multiple Positions - 1680332, Ernst & Young U.S. LLP, San Jose, CA. Solve complex analytical problems in an effort to bring data to insights and enable the use of ML and AI at scale for clients. Assist with clients' data strategy, business priorities, and success measures to provide context for designing and building models. Translate business requirements to Data Science solutions leveraging strong business acumen. Apply technical knowledge to architect solutions that meet business needs, AA/AI roadmaps, and contributing to architectures that enable the ability to scale to support additional modelling use cases. Collaborate with Microsoft Cloud Solution Architects and Data Platform Engineers in developing complex end-to-end Enterprise solutions on Microsoft Azure platform. Design machine learning pipelines that curate datasets, train, test, and validate models, compare model performance to existing or previously catalogued models, and then package the models and either deploy them to a real-time serving layer or handle batch scoring activities. Provide directional guidance and recommendations around data technology, data models, data storage and data analytics. Stay abreast of current business and industry trends relevant to the client's business. Manage and motivate teams of professionals with diverse skills and backgrounds. Consistently deliver quality client services by monitoring progress. Demonstrate in-depth technical capabilities and professional knowledge. Maintain long-term client relationships and networks. Cultivate business development opportunities Full time employment, Monday – Friday, 40 hours per week, 8:30 am – 5:30 pm.

Requirements

Must have a Bachelor’s degree in Mathematics, Information Systems, Statistics, Computer Science, Data Science, Machine Learning, Analytics, Engineering or a related field and 5 years of progressive, post-baccalaureate advisory and/or consulting work experience. Alternatively, will accept a Master’s degree in Mathematics, Information Systems, Statistics, Computer Science, Data Science, Machine Learning, Analytics, Engineering or a related field and 4 years of advisory and/or consulting work experience.
Must have 4 years of combined experience with data science, AI, and/or data engineering or big data.
Must have 2 years of combined experience with 3 of the following: - machine learning such as k-NN, naive bayes, decision trees, or SVM - data mining and statistical tools - pattern recognition and predictive modelling - recommendation engines, scoring systems, A/B testing - setting up data and experimental platforms.
Must have 1 year of experience with analytics and big data technologies within Microsoft Azure, using tools such as Azure Data Factory, Azure Machine Learning, Azure Cognitive Services, Azure Databricks, or Azure Synapse Analytics.
Must have 1 year of experience with leveraging distributed techniques for training and scoring machine learning models using Azure Databricks.
Must have 1 year of experience with any of the following cloud Machine Learning frameworks and tools such as Tensor Flow, PyTorch, ONNX, Weka, NumPy, or PyMongo.
Must have 1 year of experience with Model Management, such as Azure ML service or MLFlow and deployment of models using Azure Kubernetes Service.
Must have 1 year of experience working in Python, R, Scala, or T-SQL.
Must have 2 years of experience counselling and mentoring junior level consultants by providing structured, on-the-job feedback.
Requires domestic travel up to 70% to serve client needs.

Responsibilities

Solve complex analytical problems in an effort to bring data to insights and enable the use of ML and AI at scale for clients.
Assist with clients' data strategy, business priorities, and success measures to provide context for designing and building models.
Translate business requirements to Data Science solutions leveraging strong business acumen.
Apply technical knowledge to architect solutions that meet business needs, AA/AI roadmaps, and contributing to architectures that enable the ability to scale to support additional modelling use cases.
Collaborate with Microsoft Cloud Solution Architects and Data Platform Engineers in developing complex end-to-end Enterprise solutions on Microsoft Azure platform.
Design machine learning pipelines that curate datasets, train, test, and validate models, compare model performance to existing or previously catalogued models, and then package the models and either deploy them to a real-time serving layer or handle batch scoring activities.
Provide directional guidance and recommendations around data technology, data models, data storage and data analytics.
Stay abreast of current business and industry trends relevant to the client's business.
Manage and motivate teams of professionals with diverse skills and backgrounds.
Consistently deliver quality client services by monitoring progress.
Demonstrate in-depth technical capabilities and professional knowledge.
Maintain long-term client relationships and networks.
Cultivate business development opportunities

Benefits

comprehensive compensation
medical and dental coverage
pension and 401(k) plans
paid time off options
ﬂexible vacation policy
time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, ﬁnancial, and emotional well-being
Continuous learning
flexibility
insights, coaching and confidence
Diverse and inclusive culture