Data Scientist

Job Description

As a Data scientist, you will directly participate in the design and development of AI and Data Science solutions to meet the operational and tactical needs. You will be responsible for developing machine learning pipelines, as well as their monitoring and maintenance. You will also be responsible for deploying AI models for the organization, primarily on-premises. You will ensure the implementation of best practices in programming and machine learning within your projects. You will conduct technological monitoring to stay up-to-date with the latest developments in MLOps and machine learning. An awareness of security, ethical, and legal aspects is a plus.

As a Data scientist, you will directly participate in the design and development of AI and Data Science solutions to meet the operational and tactical needs. You will be responsible for developing machine learning pipelines, as well as their monitoring and maintenance. You will also be responsible for deploying AI models for the organization, primarily on-premises. You will ensure the implementation of best practices in programming and machine learning within your projects. You will conduct technological monitoring to stay up-to-date with the latest developments in MLOps and machine learning. An awareness of security, ethical, and legal aspects is a plus.

Desired Profile

You hold a master's or doctoral degree in computer science, AI, or equivalent and can demonstrate a minimum of 3 years of experience in data science, MLOps, and ML.

You Have Expertise In The Following Areas

• On-premise and cloud development: Expertise in the development and deployment of AI solutions on-premise and in the cloud (Azure, AWS, GCP).
• 3+ years of industry experience: You have 3+ years of experience in the world of ML, MLOps and big data with a focus on large-scale deployment.
• Theoretical background and practical expertise: in the field of ML and deep learning.
• Database Paradigm (SQL & NoSQL): Advanced knowledge of relational and non-relational databases (SQL and NoSQL), including PostgreSQL, MySQL, Milvus, Neo4j, etc.
• ML MLOps: Demonstrable experience in deploying ML models and expertise in MLOps.
• Focus on big data: experience in the exploitation of large structured and unstructured datasets .
• Containerization and deployment: Experience with Docker and Kubernetes, as well as
• orchestration tools like Kubeflow. Proficiency with ML pipelines (Kubeflow, MLflow, SageMaker, etc.).
• CI/CD for ML: Proficiency in implementing CI/CD for ML models and associated code.
• Data Storage: Experience with different data storage solutions (data lakes, data warehouse houses, object storage (S3))
• System architecture: Ability to design an end-to-end ML system taking into account scalability, robustness, maintenance and hardware constraints.

Hard Skill

• Databases: MySQL, PostgreSQL, Neo4j, Milvus
• AI/ML Frameworks & Tools: Hugging Face, MLflow, PyTorch, TensorFlow, Scikit-learn, OpenCV, vLLM
• Programming Languages: Python; R is a plus
• Orchestration & Containerization: Docker, Kubernetes, Kubeflow
• Cloud Platforms: Microsoft Azure, AWS
• Code & Model Versioning: MLflow, Git, GitHub, GitLab

Skills

• Azure
• Docker
• GIT
• GitHub
• Gitlab
• Hugging face
• Kubeflow
• Kubernetes
• kite
• MLflow
• MySql
• Neo4J
• PostgreSQL
• Python
• Pytorch
• Ruff
• TensorFlow