Technical and Soft Skill sets required:

• Study and transform data science prototypes
• Design machine learning systems
• Research and implement appropriate ML algorithms and tools
• Select appropriate datasets and data representation methods
• Run machine learning tests and experiments
• Perform statistical analysis and fine-tuning using test results
• Train and retrain systems when necessary
• Extend existing ML libraries and frameworks
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Managing available resources such as hardware, data, and personnel so that deadlines are met
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• 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 the real world
• Verifying data quality, and/or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Finding available datasets online that could be used for training
• Defining validation strategies
• Defining the pre-processing or feature engineering to be done on a given dataset
• Defining data augmentation pipelines
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production
• Keep abreast of developments in the field

Qualification requirements:

• Deep knowledge of math, probability, statistics and algorithms
• Any Graduate/ Postgraduate