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Machine Learning Engineer (Applied AI/ML)
senior · Data Science & Analytics
Build and improve machine learning models and analytics that translate data into measurable business outcomes.
Job Summary:
As a Senior Associate Data Scientist in Advanced Analytics and Machine Learning, you plan and deliver analytics projects that translate business problems into models and insights.
Job responsibilities:
- Plan, develop, and execute analytical projects using appropriate techniques
- Clarify problem statements by asking targeted questions and confirming scope, constraints, and success measures
- Partner with developers to implement models and analytics deliverables and integrate enhancements into existing solutions
- Build knowledge of business processes and modeling methods to improve solution quality and relevance
- Evaluate and monitor performance of new and existing models using defined metrics and thresholds
- Deliver models and analytics outputs on time and with documented assumptions and limitations
- Improve models and automate recurring reports and procedures to reduce manual effort
- Organize code and document model development, validation, and change history
- Explore new data sources, techniques, and environments to enhance model accuracy and robustness
- Communicate model results and analytical findings through clear presentations and visualization
Required qualifications, capabilities, and skills
- Master’s degree in statistics, computer science, engineering, or a related quantitative field or bachelor’s degree plus three years of relevant experience
- Experience with generative AI, including building agent-based workflows and retrieval-augmented generation systems
- Working knowledge of XGBoost, random forest, and k-means algorithms
- Advanced proficiency in data analysis and machine learning techniques
- Intermediate programming proficiency in SQL and Python or R
- Intermediate experience with data visualization tools (for example, Matplotlib or Tableau)
- Experience applying supervised and unsupervised learning methods in practical use cases