Research, design and implement deep learning and machine learning models to meet business needs.
Understand stakeholder requirements and translate them into ML solutions.
Write Python code and contribute to reusable machine learning pipelines.
Work closely with international teams of data scientists, ML and software engineers to deliver solutions.
Provide support for low-code/no-code solutions to enable business users where applicable.
Stay up to date with advances in data science and implement best practices across projects.
Minimum Requirements:
Qualifications/Experience:
Minimum Masters Degree in Data Science, Computer Science, Statistics, Engineering or a related field with strong mathematical foundations.
3–5 years of hands-on experience in data science, machine learning and applied AI.
Essential Skills Requirements:
Proficiency in Python for data science and production code development.
Strong experience with classical machine learning algorithms and applied AI techniques.
Practical experience building and training deep learning models using TensorFlow.
Familiarity with MLOps practices for deploying and managing models in production.
Experience with data engineering tasks: ETL, preprocessing, feature engineering and visualization.
Strong analytical and problem-solving capabilities with attention to performance, efficiency and scalability.
Experience engaging with stakeholders and translating business needs into technical solutions.
Advantageous Skills Requirements:
Hands-on experience with cloud platforms such as AWS and Azure.
Experience with ML frameworks beyond TensorFlow (e.g., PyTorch, Keras, scikit-learn).
Desired Skills:
- Python
- Machine Learning Algorithms
- TensorFlow