Data Scientist with ML Ops

 Educational Background: Bachelor’s or master’s degree in computer science, Data Science, Engineering, Mathematics, or a related field.
· Experience: 6-9 years of experience in data science, with a strong focus on MLOps and productionizing machine learning models.
· Programming Skills: Proficiency in Python for data analysis and machine learning.
· Machine Learning Expertise: Deep understanding of machine learning algorithms, statistical modeling, and model evaluation techniques.
· MLOps Knowledge: Very good knowledge of MLOps principles, tools, and practices, including real-time usage and deployment strategies. Hands-on experience with MLOps platforms such as MLflow, Kubeflow, TensorFlow Serving, or similar.
· Cloud Platforms: Experience with major cloud providers (AWS, Azure, Google Cloud) for deploying and managing machine learning models.
· Data Engineering Skills: Solid understanding of data engineering principles, including ETL processes, data warehousing, and SQL.
· Version Control: Proficiency in using version control systems such as Git for code management.
· Communication Skills: Strong verbal and written communication skills with the ability to present technical information to diverse audiences.
Good-to-Have Qualifications:
· Big Data Technologies: Experience with big data tools and technologies like Hadoop, Spark, or Kafka.
· Containerization & Orchestration: Familiarity with Docker, Kubernetes, or other containerization and orchestration technologies.
· DevOps Practices: Knowledge of DevOps methodologies and tools such as Jenkins, Terraform, or CI/CD pipelines.
· Business Acumen: Ability to understand and translate business requirements into technical solutions and model designs.