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AI ML Architect
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#340758
Full Time
Job Description
At Data Dynamics, we take a holistic approach to managing the explosive growth of unstructured data! We care about delivering storage optimization and data optimization. Our Unified Unstructured Data Management Platform is an industry leader in Unstructured Data Management and our journey has just begun.
With over 300 clients operating in 6 continents, we have an enormous amount of F1000 user feedback. Like our clients, we believe in the power of Analytics!
Empower clients with metadata-based analysis, movement, management and content awareness for all unstructured data assets
Deliver on existing SLA’s while improving existing CAPEX and OPEX unstructured data budgets
Eliminate file type, storage or Cloud vendor lock-in with heterogeneous data movement, on-premise, on-premise to cloud or cloud to cloud
Provide a NEW level of content insights to protect clients from unknown data exposure risks (GRC, PII, HIPAA, GDPR, etc.)
Data Dynamics’ four pillars of client impact: Analyze, Move, Manage and Modernize are not only applicable to our customers. We care first and foremost about the career and personal successes of our employees. At Data Dynamics our interview process is designed to produce candidate and company insights into existing skills, experience and personal objectives. We will move fast and competitively when we discover the right candidate.
Responsibilities
Deliver insightful presentations of analysis and business results to business unit leaders
Develop analytics proof of concepts on emerging trends in different vertical areas to develop new analytical PoCs for Competency centre
Evaluate and compare algorithm performance based on large, real-world data sets
Work with large volumes of data and apply ML techniques towards real product and business problems
Work with product/business owners to map business requirements into products productized solutions and/or working prototypes of ML algorithms
Leverage data to analyze the business unit function, identify the business drivers and recommend strategy and future course of actions
Conceptualize modeling framework, define data requirement, create predictive variables and co-ordinate model implementation with other business partners, and create experimental designs to understand drivers of business performance.
Hands on programming in Python, SAS and/or R
Prior experience in writing model documents is strongly preferred but not required
Timely training Analytics resources and sharing best practices with them
Required Skills and Experience
12 years of experience in architecting AI applications with Machine Learning, NLP, Deep Learning with Python, R, Predictive Modelling, ANN, CNN, RNN, SOTA, Advanced CNN and Advanced RNN with LSTM, Encoders, Decoders, Attention Models, Transformers, BERT, ROBERTA, AUTO ML, h20.ai, Pycaret.
Skilled in libraries such as Pytorch, Tensorflow, Sklearn, Numpy, Pandas, Matplotlib, for Data Visualization.
Worked Independently for Deployment of Models using Dockers and Kubernetes in GCP, ML Flow, Azure, AWS, involving Model Scoring, Model Management, Model Drifting, Model Retraining Mechanisms.
Conceptualize modelling framework, define data requirements, feature engineering & extraction, co-ordinate model implementation with other business partners, and create experimental designs to understand drivers of business performance.
Deliver insightful presentations of analysis and business results to business unit leaders
Develop analytics proof of concepts on emerging trends in different vertical areas to develop new analytical PoCs for Competency centre
Evaluate and compare algorithm performance based on large, real-world data sets
Work with large volumes of data and apply ML techniques towards real product and business problems
Work with product/business owners to map business requirements into products / productized solutions and/or working prototypes of ML algorithms
Leverage data to analyze the business unit function, identify the business drivers and recommend strategy and future course of actions
Conceptualize modeling framework, define data requirement, create predictive variables and co-ordinate model implementation with other business partners, and create experimental designs to understand drivers of business performance.
Desired Skills and Experience:
Excellent communication skills, both verbal and written
Strong analytical and problem-solving skills
Expertise in developing business presentations
Hiring Insights
Job activity
Posted Today
Data Dynamics
With over 300 clients operating in 6 continents, we have an enormous amount of F1000 user feedback. Like our clients, we believe in the power of Analytics!
Empower clients with metadata-based analysis, movement, management and content awareness for all unstructured data assets
Deliver on existing SLA’s while improving existing CAPEX and OPEX unstructured data budgets
Eliminate file type, storage or Cloud vendor lock-in with heterogeneous data movement, on-premise, on-premise to cloud or cloud to cloud
Provide a NEW level of content insights to protect clients from unknown data exposure risks (GRC, PII, HIPAA, GDPR, etc.)
Data Dynamics’ four pillars of client impact: Analyze, Move, Manage and Modernize are not only applicable to our customers. We care first and foremost about the career and personal successes of our employees. At Data Dynamics our interview process is designed to produce candidate and company insights into existing skills, experience and personal objectives. We will move fast and competitively when we discover the right candidate.
Responsibilities
Deliver insightful presentations of analysis and business results to business unit leaders
Develop analytics proof of concepts on emerging trends in different vertical areas to develop new analytical PoCs for Competency centre
Evaluate and compare algorithm performance based on large, real-world data sets
Work with large volumes of data and apply ML techniques towards real product and business problems
Work with product/business owners to map business requirements into products productized solutions and/or working prototypes of ML algorithms
Leverage data to analyze the business unit function, identify the business drivers and recommend strategy and future course of actions
Conceptualize modeling framework, define data requirement, create predictive variables and co-ordinate model implementation with other business partners, and create experimental designs to understand drivers of business performance.
Hands on programming in Python, SAS and/or R
Prior experience in writing model documents is strongly preferred but not required
Timely training Analytics resources and sharing best practices with them
Required Skills and Experience
12 years of experience in architecting AI applications with Machine Learning, NLP, Deep Learning with Python, R, Predictive Modelling, ANN, CNN, RNN, SOTA, Advanced CNN and Advanced RNN with LSTM, Encoders, Decoders, Attention Models, Transformers, BERT, ROBERTA, AUTO ML, h20.ai, Pycaret.
Skilled in libraries such as Pytorch, Tensorflow, Sklearn, Numpy, Pandas, Matplotlib, for Data Visualization.
Worked Independently for Deployment of Models using Dockers and Kubernetes in GCP, ML Flow, Azure, AWS, involving Model Scoring, Model Management, Model Drifting, Model Retraining Mechanisms.
Conceptualize modelling framework, define data requirements, feature engineering & extraction, co-ordinate model implementation with other business partners, and create experimental designs to understand drivers of business performance.
Deliver insightful presentations of analysis and business results to business unit leaders
Develop analytics proof of concepts on emerging trends in different vertical areas to develop new analytical PoCs for Competency centre
Evaluate and compare algorithm performance based on large, real-world data sets
Work with large volumes of data and apply ML techniques towards real product and business problems
Work with product/business owners to map business requirements into products / productized solutions and/or working prototypes of ML algorithms
Leverage data to analyze the business unit function, identify the business drivers and recommend strategy and future course of actions
Conceptualize modeling framework, define data requirement, create predictive variables and co-ordinate model implementation with other business partners, and create experimental designs to understand drivers of business performance.
Desired Skills and Experience:
Excellent communication skills, both verbal and written
Strong analytical and problem-solving skills
Expertise in developing business presentations
Hiring Insights
Job activity
Posted Today
Data Dynamics
