Qualifications:
5-7 years of professional experience in machine learning, data science, or related roles.
Should have good exposure and understanding in time series Modelling using ARIMA, ARIMAX
Exposure into how to handle underfitting and overfitting.
Should be capable of applying techniques which helps to generalize Models.
Regularization techniques LASSO, RIDGE & ELASTIC NET and when to apply them.
Good exposure in Unsupervised machine learning like clustering, dimensionality reduction, Outlier detection
Ability to understand how Models are optimized using various techniques including Gradient Descent approach.
Good understanding of deep learning algorithms CNN, RNN, LSTM and how to control overfitting in such cases.
Good hands on in data engineering to process huge scale of data using Big Data (Spark/Hive)
Good coding practices to write production ready code for creating data pipeline for Models to consume.
Very good hands on in python (Pandas/Numpy/Scikit-Learn/NLTK/spaCy/Matplotlib)
Able to apply the right level of ML techniques for the given problem statement.
Ability to access information contained in data and engineer appropriate features.
Familiar with Python language and various platforms for hosting ML models
Expert in model training, tuning and validation.
Expert in statistical techniques, deep learning methodologies, GenAI, alternate techniques such as Bayesian etc.
Exposure to big data and related models
Ability to articulate model choice and convert outcome for business decision making.
Expert in Model Development Lifecycle from sourcing to model monitoring
Ability to create code that is highly performance in the given platform.
Ability to map model and business use case to the appropriate platform and tools needed.
Understanding of technical and machine learning governance
Ability to validate and articulate model choices with relevant metrics (precision, recall, confusion matrix, RMSE,