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Tech Stack
Tools & technologiesNumpyPandasPythonPyTorchScikit-LearnTensorflow
About the role
Key responsibilities & impact- Implement machine learning models and data pipelines based on guidance from senior engineers and scientists
- Assist in converting research prototypes into reliable, scalable production workflows
- Support development of data pipelines and feature engineering workflows
- Contribute to model training, evaluation, and performance tuning
- Deploy and monitor ML models in batch and near real-time environments
- Debug issues in data pipelines and model performance with support from senior team members
- Write clean, maintainable, and well-documented code
- Collaborate with software engineers to integrate ML components into production systems
Requirements
What you’ll need- Degree in Computer Science, Statistics, Engineering, or related field (Masters preferred)
- Experience in machine learning, data science, or related area (internships included)
- Proficiency in Python and familiarity with ML libraries (such as Numpy, Pandas, Scikit-learn)
- Experience with deep learning frameworks (TensorFlow and/or PyTorch) and/or machine learning workflows
- Knowledge of core Machine Learning and AI models
- Strong problem-solving skills and willingness to learn
- Exposure to advanced ML techniques and large-scale optimization problems (highly preferred)
- Experience deploying, monitoring, and maintaining ML models for batch and real-time inference (highly preferred)
- Experience in pricing, revenue management, and offer optimization (highly preferred)
Benefits
Comp & perks- Flexible ways of working
- Continuous learning opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningdata pipelinesfeature engineeringmodel trainingmodel evaluationperformance tuningPythonNumpyPandasScikit-learn
Soft Skills
problem-solvingwillingness to learn
