Sr. Machine Learning Engineer

at  EXPERIAN

Full-time
Employee Status: Regular
Role Type: Hybrid
Job Posting - Salary Range: $83,093 - $144,028
Department: Analytics
Flexible Time Off: 15 Days
Schedule: Full Time

COMPANY DESCRIPTION

We are thrilled to share that FORTUNE has named Experian one of the 100 Best Companies to Work For. In addition, for the last five years we’ve been name in the top 100 “World’s Most Innovative Companies” by Forbes Magazine.
This position will be supporting the Experian Consumer Services (ECS) - a passionate and innovative team with a mission to provide Financial Power to All™. Our portfolio offers credit education and identity protection solutions to consumers and helps businesses manage the impact of a data breach.

JOB DESCRIPTION

The Senior ML Engineer is primarily responsible for high-quality Python and SQL scripts needed to bring machine learning solutions to life. As a key team member, you will work with 1-3 other developers and engineers to gather project requirements, collaborate on solution design and strategy, identify essential architectural components, organize tasks, and contribute directly to all parts of the codebase. As a senior member of a highly professional team, you will work on all aspects of the project lifecycle encompassing everything from data discovery to performance monitoring and executive communications.

Responsibilities:

• Organize project tasks, requirements, documentation and progress through Jira and Confluence.
• Work with stakeholders to gather requirements, communicate status and share insights.
• Build high-quality Tableau dashboards to help end users engage with model output and understand recommendations.
• Perform data discovery and analysis to explore and define new use cases.
• Use applied mathematics and quantitative skills as needed for modeling tasks, impact analysis, scenario planning, forecasting etc.
• Develop, train, and deploy statistical and machine learning models for revenue, campaign optimization, customer LTV, click propensity, action sequencing, personalization, and segmentation.
• Maintain an organized code base using GitHub, Jira and Confluence.
• Develop solution modules and subsystems for preprocessing, measurement, drift, error handling, and anomaly detection.
• Develop data pipelines by creating efficient data models, writing high-quality ETL/SQL, building Alteryx workflows, managing tickets, and collaborating with partners.

QUALIFICATIONS

• High proficiency in Python with 5+ years of development experience in modeling or data engineering
• Excellent knowledge of SQL with 5+ years of experience in database and data validation
• Working knowledge of Tableau with 2+ years of experience using BI tools for analysis and presentation
• Working knowledge of Alteryx
• Hands-on experience with deep learning architecture, e.g. transformers, LSTMs and convolutions
• 2+ years of experience working with DL frameworks like PyTorch and Tensorflow
• Wide familiarity and experience with statistical and mathematical concepts, esp. probability, systems of equations (LA), and optimization theory
• Firm understanding of model containerization with some experience using Docker
• Familiarity with the AWS ecosystem preferred: Redshift, S3, EC2, Sagemaker and Lambda
• Strong track record communicating with partners and stakeholders at all levels
• Independent, organized, accountable and proactive

• Organize project tasks, requirements, documentation and progress through Jira and Confluence.
• Work with stakeholders to gather requirements, communicate status and share insights.
• Build high-quality Tableau dashboards to help end users engage with model output and understand recommendations.
• Perform data discovery and analysis to explore and define new use cases.
• Use applied mathematics and quantitative skills as needed for modeling tasks, impact analysis, scenario planning, forecasting etc.
• Develop, train, and deploy statistical and machine learning models for revenue, campaign optimization, customer LTV, click propensity, action sequencing, personalization, and segmentation.
• Maintain an organized code base using GitHub, Jira and Confluence.
• Develop solution modules and subsystems for preprocessing, measurement, drift, error handling, and anomaly detection.
• Develop data pipelines by creating efficient data models, writing high-quality ETL/SQL, building Alteryx workflows, managing tickets, and collaborating with partners