About the job
Responsaibilities
- Design and implement scalable, efficient, and maintainable machine learning models using Python and relevant libraries (e.g., scikit-learn, TensorFlow).
- Utilize Apache Spark (PySpark) for distributed data processing on large datasets.
- Apply statistical techniques to assess model performance and ensure robustness.
- Mentor junior data scientists, providing technical guidance and support in best practices.
- Present findings and recommendations to stakeholders and senior management in a clear and actionable manner.
- Stay up-to-date with the latest developments in machine learning, data science, and related technologies to drive innovation within the team.
- Ensure best practices for version control, reproducibility, and model monitoring in production environments.
- Advanced degree (Master's or Ph.D.) in Computer Science, Statistics, Mathematics, or a related field.
- 5+ years of hands-on experience in building, deploying, and maintaining machine learning models in production.
- Strong expertise in machine learning algorithms and statistical modeling.
- Proficiency in the Python machine learning ecosystem (scikit-learn, TensorFlow, PyTorch, etc.).
- Experience with Apache Spark (PySpark) for large-scale data processing.
- Deep understanding of Object-Oriented Programming (OOP) principles and software development best practices.
- Experience working with cloud platforms like Azure and using Databricks for data science workflows.
- Familiarity with Deep Learning algorithms and techniques.