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

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