Machine learning job requirements summary

Design, build, deploy and scale Machine Learning models in production.

Qualification: Graduate degree in Computer Science << Masters << PhD

Technical Skills

1. Language: Python, C++
2. Deep Learning Framework: Keras, PyTorch, Tensorflow
3. Libraries: Scikit-Learn, Numpy, Pandas, XGBoost ……
4. ML DevOps: Docker, Kubernetes, CI/CD
5. Testing: Metrics identification, A/B Testing
6. Cloud: GCP, AWS, Azure

Soft-Tech Skills

1. Version management system e.g. Git
2. Model version management e.g. Tensorflow serving
3. Cross-team collaboration.
4. Code review.
5. Technical documentation.
6. Mentoring and self-learning.

Soft Skills

1. Storytelling with data and visualizations.
2. Communication with team and stakeholders.
3. Scope development.
4. Team management.
5. Effective organization.

Hidden requirements/skills

1. Data storage, loading and cleaning at scale.
2. Data augmentation.
3. Working with a small dataset or missing information.
4. Building tools for data handling, presentation and proof of concepts.
5. API development and deployment for exposing the model as a service.
6. Software engineering skills.
7. Data structures & algorithms.
8. In sync with the latest research and technological innovation.

Proof of experience

1. Research papers.
2. Active participation in data science platforms e.g. Kaggle, DrivenData etc.
3. Open-source contributions.
4. Availability of developed solution for the recruiter.

Apart from above Job requirements vary on the type of problems being tackled; Text, Image, Audio, Video etc.

Reference: To generate the above structure, I have read through around hundreds of job description by searching Linkedin with keyword “Machine Learning”, “Data Scientist”, “Deep Learning”, “Natural Language Processing”.

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