Roles & Responsibilities:
- Collaborate with data scientists to develop, train, and evaluate machine learning models.
- Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.
- Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment.
- Implement DevOps/MLOps best practices to automate ML workflows and improve efficiency.
- Develop and implement monitoring systems to track model performance and identify issues.
- Conduct A/B testing and experimentation to optimize model performance.
- Work closely with data scientists, engineers, and product teams to deliver ML solutions.
- Guide and mentor junior engineers in the team
- Stay updated with the latest trends and advancements
What we expect of youWe are all different, yet we all use our unique contributions to serve patients.
Basic Qualifications:
- Doctorate degree and 2 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR
- Master s degree and 8 to 10 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR
- Bachelor s degree and 10 to 14 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR
- Diploma and 14 to 18 years of years of Computer Science, Statistics, and Data Science, Machine Learning experience
Preferred Qualifications:
Must-Have Skills:
- Strong foundation in machine learning algorithms and techniques
- Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
- Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills
Good-to-Have Skills:
- Experience with big data technologies (e.g., Spark), and performance tuning in query and data processing
- Experience with data engineering and pipeline development
- Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification
- Knowledge of NLP techniques for text analysis and sentiment analysis
- Experience in analyzing time-series data for forecasting and trend analysis
- Familiar with AWS, Azure, or Google Cloud;
- Familiar with Databricks platform for data analytics and MLOps
Professional Certifications
- Cloud Computing and Databricks certificate preferred
Soft Skills:
- Excellent analytical and fixing skills.
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation.
- Ability to manage multiple priorities successfully.
- Team-oriented, with a focus on achieving team goals
- Strong presentation and public speaking skills.
