Data Science Roles

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Data Scientist

Expertise in AI/ML solution spaces in one or more of the following areas: deep learning, reinforcement learning, evolutionary optimization, NLP, computer vision, classical machine learning, tree search methods
Ability to build repeatable/modularized model training and inference pipelines
Ability to build repeatable/modularized data transformation pipelines for model training and inference pipelines, working closely with data engineers in building these pipelines
Expertise with MLOps tools such as MLFlow, Neptune, etc, for the purposes of experiment tracking, model registries, feature stores, etc. Ability to deploy models as server/serverless entities.
Responsible for model health, data drift, and logging. Responsible for debugging models and routinely checking them for accuracy or error.
Provide thought leadership about AI technologies and capabilities
Stay up to date with AI research in their respective areas, comfortable reading papers and summarizing new knowledge for the team
Situational awareness of how an AI modeling process can be built into a product for customers

Ability to lead more junior members in data science engagements
Deep expertise in AI/ML solution spaces in several of the following areas: deep learning, reinforcement learning, evolutionary optimization, NLP, computer vision, classical machine learning, tree search methods
Strong ability to build repeatable/modularized model training and inference pipelines
Strong ability to build repeatable/modularized data transformation pipelines for model training and inference pipelines, working closely with data engineers in building these pipelines
Deep expertise with MLOps tools such as MLFlow, Neptune, etc, for the purposes of experiment tracking, model registries, feature stores, etc. Ability to deploy models as server/serverless entities.
Responsible for model health, data drift, and logging. Responsible for debugging models and routinely checking them for accuracy or error.
Provide thought leadership about AI technologies and capabilities. Take lead on marketing initiatives via webinars and blog posts.
Stay up to date with AI research in their respective areas, comfortable reading papers and summarizing new knowledge for the team
Situational awareness of how an AI modeling process can be built into a product for customers. Builds relationships with client DS teams.

Takes leadership of data science engagements. Owns a workflow supported by Empower. Builds relationships with client DS teams.
Deep expertise in AI/ML solution spaces in a wide-variety of the following areas: deep learning, reinforcement learning, evolutionary optimization, NLP, computer vision, classical machine learning, tree search methods
Strong ability to build repeatable/modularized model training and inference pipelines
Strong ability to build repeatable/modularized data transformation pipelines for model training and inference pipelines, working closely with data engineers in building these pipelines
Deep expertise with MLOps tools such as MLFlow, Neptune, etc, for the purposes of experiment tracking, model registries, feature stores, etc. Ability to deploy models as server/serverless entities.
Provide thought leadership about AI technologies and capabilities. Take lead on marketing initiatives via webinars and blog posts.
Stay up to date with AI research in their respective areas, comfortable reading papers and summarizing new knowledge for the team
Situational awareness of how an AI modeling process can be built into a product for customers.

Responsible for presales and delivery of data science engagements.
Leads the overarching Empower ML initiatives. Creates the strategy and roadmap for the platform.
Identifies and drives the creation of new workflows to improve Empower ML initiative.
Works with seniors/leads to build and maintain client relationships.
Deep expertise in AI/ML solution spaces in a wide-variety of the following areas: deep learning, reinforcement learning, evolutionary optimization, NLP, computer vision, classical machine learning, tree search methods
Provide thought leadership about AI technologies and capabilities. Take lead on marketing initiatives via webinars and blog posts.
Stay up to date with AI research in their respective areas, comfortable reading papers and summarizing new knowledge for the team

Expertise in data engineering – Databricks, SQL, python, Azure Data Factory, etc
Ability to build data transformation/embedding pipelines, working closely with data scientists to build and monitor these pipelines. Comfortable with MLOps tools like MLFLow (maybe not to the degree of a DS) for the purposes of data embedding pipeline creation.
Create repeatable/modularized data modelling pipelines as required by the data scientist/AI modeling requirements
Ensure that data transformation pipelines mesh well with existing infrastructure
Ability to build data monitoring health checks for newly ingested data
Familiarity with supporting DS teams
Stay up to date with new data engineering products and technologies. Ability to digest and summarize this knowledge for the team.
Troubleshoot production errors and provide on time support to client deployments
Situational awareness of how an AI modeling process can be built into a product for customers

Strong expertise in data engineering – Databricks, SQL, python, Azure Data Factory, etc
Ability to lead more junior members in data science engagements
Be a leader for internal engagements, work across teams to build good IP
Ability to build data transformation/embedding pipelines, working closely with data scientists to build and monitor these pipelines. Comfortable with MLOps tools like MLFLow (maybe not to the degree of a DS) for the purposes of data embedding pipeline creation.
Create repeatable/modularized data modelling pipelines as required by the data scientist/AI modeling requirements
Ensure that data transformation pipelines mesh well with existing infrastructure
Leads building data monitoring health checks for newly ingested data
Familiarity with supporting DS teams
Stay up to date with new data engineering products and technologies. Ability to digest and summarize this knowledge for the team.
Troubleshoot production errors and provide on time support to client deployments
Situational awareness of how an AI modeling process can be built into a product for customers