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,