Note: The job is a remote job and is open to candidates in USA. Capgemini is a global leader in engineering services, seeking a highly capable Senior ML Engineer to support revenue generation and cost optimization projects. The role involves improving code, creating automated testing, and developing reusable frameworks for machine learning applications.
Responsibilities
- Build, maintain, and document machine learning frameworks (python packages) used across multiple projects
- Support a project team with Data Scientists, Business Stakeholders, Analysts, and Data Engineers
- Develop reusable feature stores for rules-based and AI/ML models
- Implement monitoring capabilities for model performance and effectiveness in production
- Automate CI/CD testing and deployments incorporating MLOps best practices
Skills
- Bachelor's degree in software engineering, computer science, data science, mathematics, or a related field
- 5+ years of overall experience in Data Analytics
- Sharp critical thinking skills and ability to learn and question complex processes and solutions
- Experience building scalable machine learning systems and data-driven products working with cross-functional teams
- Experience creating python packages
- Well-developed software engineering skills, including use of proper development, QA, and production environments, object-oriented programming, version control, and knowledge of multiple programming languages
- Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark)
- Proficiency in SQL
- Master's or PhD degree in computer science, data science, mathematics, or a related field
- Experience with sensor data or other noisy data types
- Experience with Agile Software Development
- Experience in a large corporation or consulting firm with focus in marketing strategies, modeling, CRM and management sciences/statistics highly desired
- Familiarity with frameworks and languages designed for big-data analytics, including Spark and Azure Data Factory
- Experience with MLOps and ML experiment tracking tools, such as Azure DevOps and MLFlow or similar
- Experience with cloud computing services such as Microsoft Azure, Amazon Web Services and/or Google Cloud Platform
- Familiarity with different data science techniques: statistics, machine learning, or cognitive AI
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