Job description 

In one sentence

This is a hands-on position for a motivated and talented innovator. The Data Scientist performs data mining and develops algorithms that provide insight from data.

What will your job look like?

Team Leadership: Mentor and guide a team of MLOps engineers, fostering collaboration and a culture of continuous improvement.
Customer and Team Communication: Act as a key point of contact for customer interactions, translating technical requirements into actionable plans and ensuring customer satisfaction. Collaborate effectively with cross-functional teams, including data scientists, software engineers, and DevOps professionals, to seamlessly integrate machine learning models into Azure-based production applications.
Model Deployment: Oversee the deployment of machine learning models into Azure-based production environments, ensuring scalability, reliability, and performance.
Automation: Design and implement automated pipelines for model training, testing, and deployment.
Monitoring and Maintenance: Establish robust monitoring and alerting systems to proactively identify and address model performance issues. Manage model retraining and updates.
Security and Compliance: Ensure all MLOps processes adhere to security and compliance.
Collaboration: Collaborate closely with cross-functional teams, including data scientists and software engineers, to seamlessly integrate machine learning models into Azure-based production applications.
Azure ML and MLflow Expertise: Leverage your deep understanding of Azure Machine Learning services and MLflow to design, implement, and optimize ML pipelines, model training, and deployment workflows enabling efficient tracking, reproducibility, and model versioning.

All you need is...

Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
Proven experience (4+ years) in machine learning operations, with at least two years of that experience in a leadership role, leading an MLOps team.
Expertise in Azure Machine Learning services, including model deployment, Azure DevOps
Proficiency in Python and scripting for automation, with a deep understanding of MLflow for tracking and managing machine learning experiments.
Hands-on experience with cloud platforms, particularly Azure, and containerization technologies.
Strong problem-solving skills and the ability to collaborate effectively in a team environment.
Exceptional communication skills to convey complex technical concepts to non-technical stakeholders.
Relevant certifications such as Microsoft Certified: Azure AI Engineer Associate or Azure Data Engineer Associate are a plus.
Requirements:

•    Education:
Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
•    Experience:
Proven experience of 2+ years as a data scientist, machine learning engineer, or similar role.
Experience deploying machine learning models in production environments.
•    Technical Skills:
o    Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
o    Strong programming skills in Python and experience with relevant libraries.
o    Knowledge of version control systems (e.g., Git).
o    Experience with MLOps tools and platforms, such as MLflow, Kubeflow, or Apache Airflow.
o    Understanding of cloud computing platforms like Databricks, AWS, Azure, or Google Cloud.
•    Soft Skills:
o    Strong problem-solving and analytical skills.
o    Excellent communication and teamwork abilities.
o    Adaptability to a dynamic and fast-paced environment.
o    Attention to detail and a commitment to quality.
o    Certifications in relevant areas (e.g., AWS Certified Machine Learning Specialty).
•    Preferred Qualifications:
o    Previous experience with continuous integration/continuous deployment (CI/CD) pipelines.
o    Knowledge of DevOps practices and tools.
o    Understanding of data engineering and ETL processes.
o    Certifications in relevant areas (e.g., AWS Certified Machine Learning Specialty).