JOB DETAILS

1) Understanding busisness objectives and developing models that help to achieve them, along with metrics to track their progress.
2) Utilize the existing frameworks, standards, patterns to create architectural foundation and services necessary for AI applications that scale from multi-user to enterprise class.
3) Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability.
4) 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.
5) Verifying data quality, and/or ensuring it via data cleaning.
6) Supervising the data acquisition process if more data is needed.
7) Finding available datasets online that could be used for training.
8) Defining validation strategies.
9) Defining the preprocessing or feature engineering to be done on a given dataset.
10) Defining data augmentation pipelines.
FUNCTIONAL AREA
Data scientist, data quality, data acquisition, data engineer, AI/ML frameworks,



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