·   Create machine learning models and applications. 
·   Perform statistical analysis and fine-tuning using test results.
·   Run machine learning tests and experiments.
·   Keep abreast of developments in machine learning and statistics.

- Understanding project objectives and developing models that help to achieve them, and
developing metrics to track their progress.
- Managing available resources such as hardware, data, and personnel so that deadlines are met
- Exploring and visualizing data to gain an understanding of it, then identifying differences in data
distribution that could affect performance when deploying the model in real world
- Verify data quality and supervise data acquisition when it is needed
- Finding available datasets online that could be used for training
- Defining the processing or feature engineering to be done on a given dataset
- Defining data augmentation pipelines
- Training models and tuning their hyper parameters
- Analyzing the errors of the model and designing strategies to overcome them
- Deploy models to production
- Developing fraud detection systems
- Designing projects, leading , coordinating and managing staff team
- Provide training for team members and also get additional trainings