Job description

• Drive technical roadmap to extend risk monitoring across identified threat surfaces. • Develop/experiment/ship state-of-the-art prediction models • Use excellent data science practices to iteratively produce high performing models • Create immediate impact through sound and practical deliveries of risk monitors • Work with engineering colleagues to convey findings through data visualizations • Measure, tune and refine existing algorithms to incrementally improve performance • Analyze new and existing data after extracting. transforming and combining it in novel ways • Convey needs to engineering and operations teams to ensure healthy feedback loops • Attract and onboard new talent while preserving and enhancing existing culture • Build strong partnerships and collaborate with other teams across the enterprise • Demonstrate sense ownership and personal accountability for your team's work Basic Qualifications: • 5+ years applied data science experience, including 3 years of advanced analytics experience focused on enterprise-specific problem solving • 2+ years management, mentoring, or other closely related team or people leadership experience • Experience in machine learning (supervised, semi-supervised or unsupervised learning) • Strong communication, delivery management, and leadership skills Preferred Qualifications • A bachelor’s degree, MSc or Ph.D. in Statistics, Data Science, Artificial Intelligence, or equivalent alternative education or experience • Applied experience with SQL, also Python or R or Scala, and modern data science tools/packages e.g. PyTorch, Transformers, TensorFlow, scikit-learn • Applied experience with Databricks and/or Azure ML • Strong coding abilities in one or more scripting languages like Python or SQL • Understanding of compliance, security, and risk domains along with associated patterns and data elements • Understanding of product and services activation, use, and transaction models and data • Understanding of statistical analysis and machine learning tools and practices • Understanding of Cloud-centric data processing and visualization approaches including SQL and NoSQL databases with exposure to Azure SQL, Azure Cosmos DB, Data Factory, Synapse, Azure Data Lake, etc • Familiarity with Agile software delivery including application lifecycle mgmt (SAFe, Azure DevOps/VSTS, Git) All your information will be kept confidential according to EEO guidelines.

Requirements

Must-haves

Drive technical roadmap to extend risk monitoring across identified threat surfaces.
Develop/experiment/ship state-of-the-art prediction models
Use excellent data science practices to iteratively produce high performing models
Create immediate impact through sound and practical deliveries of risk monitors
Work with engineering colleagues to convey findings through data visualizations
Measure, tune and refine existing algorithms to incrementally improve performance
Analyze new and existing data after extracting. transforming and combining it in novel ways
Convey needs to engineering and operations teams to ensure healthy feedback loops
Attract and onboard new talent while preserving and enhancing existing culture
Build strong partnerships and collaborate with other teams across the enterprise
Demonstrate sense ownership and personal accountability for your team's work
5+ years applied data science experience, including 3 years of advanced analytics experience focused on enterprise-specific problem solving
2+ years management, mentoring, or other closely related team or people leadership experience
Experience in machine learning (supervised, semi-supervised or unsupervised learning)

Nice-to-haves

A bachelor’s degree, MSc or Ph.D. in Statistics, Data Science, Artificial Intelligence, or equivalent alternative education or experience
Applied experience with SQL, also Python or R or Scala, and modern data science tools/packages e.g. PyTorch, Transformers, TensorFlow, scikit-learn
Applied experience with Databricks and/or Azure ML
Strong coding abilities in one or more scripting languages like Python or SQL
Understanding of compliance, security, and risk domains along with associated patterns and data elements
Understanding of product and services activation, use, and transaction models and data
Understanding of statistical analysis and machine learning tools and practices
Understanding of Cloud-centric data processing and visualization approaches including SQL and NoSQL databases with exposure to Azure SQL, Azure Cosmos DB, Data Factory, Synapse, Azure Data Lake, etc
Familiarity with Agile software delivery including application lifecycle mgmt (SAFe, Azure DevOps/VSTS, Git)
All your information will be kept confidential according to EEO guidelines.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningsqlpythonazuremlagile

Platform: smartrecruiters
Effort: medium