Data Scientist

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

**Job**Description**:
**Responsibilities**:

- **
Model Development & Deployment**:

- Collaborate with data scientists and engineers to design, build, and deploy machine learning models at scale.
- Develop and maintain MLOps/AIOPs pipelines to automate the end-to-end lifecycle of machine learning models (from development to deployment, monitoring, and retraining).
- Work on the integration of models into production systems while ensuring scalability, security, and performance.
- ** Model Operationalization**:

- Implement CI/CD pipelines for ML models, ensuring smooth deployments with mínimal downtime.
- Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew.
- Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations.
- ** Optimization & Scalability**:

- Optimize machine learning models and pipelines for performance and cost efficiency (compute, storage).
- Manage infrastructure for ML workloads using cloud-native tools (Azure, Kubernetes, Docker) or other container orchestration platforms.
- ** Collaboration & Communication**:

- Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions.
- Provide technical guidance to junior engineers and drive best practices for MLOps/AIOPS within the team.
- ** Security & Compliance**:

- Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards.
- Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA).

**Qualifications**:
**Required Skills**:

- 4+ years of experience in machine learning, MLOps/AIOPs, or software engineering roles.
- Proven track record of deploying large-scale machine learning systems in production.
- Strong experience with cloud platforms (Azure preferred) and infrastructure as code (e.g., Terraform, ARM templates).
- Advanced knowledge of MLOps/AIOPs practices, including pipeline automation, monitoring, and orchestration.
- Experience optimizing ML models for performance and scalability in production environments.
- Demonstrated ability to lead initiatives, mentor junior team members, and influence cross-functional teams.
- Solid understanding of security and compliance frameworks relevant to ML operations.
- Hands-on experience in building and deploying ML models in a cloud environment (preferably Azure).
- Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch).
- Experience with containerization (Docker, Kubernetes) and microservices architecture.
- Strong knowledge of CI/CD tools and workflows (Azure DevOps, GitHub Actions).
- Basic understanding of model monitoring, retraining, and model governance practices.

**Desired Skills**:

- Experience with Azure Machine Learning, Azure Fabric, Synapse, or similar platforms.
- Strong understanding of data versioning, governance, and reproducibility in ML workflows.
- Knowledge of responsible AI practices, including fairness, transparency, and bias mitigation.
- Strong communication skills and the ability to work in a fast-paced, collaborative environment.

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