Responsibilities:

- Passion for learning new technologies and be up to date with the scientific research community.

- Work in technical teams in development, deployment, and application of machine learning solutions, leveraging technical components and explaining the modelling decisions

- Take responsibility for insights, reports and explanability of the decisions taken by predictive models

- Responsible for taking an idea from concept to production thoroughly with feedback from all stakeholders.

Skills Required:

- Must have minimum of 5-7 years of industry experience in developing data science models.

- Deep understanding and experience in the field of Machine Learning, Deep Learning and statistical learning

- The person should be excellent at Classification (logistic regression, svm, decision tree, random forest, neural network), Regression (linear regression, decision tree, random forest, neural network), Classical optimisation (gradient descent, newton rapshon, etc), Graph theory (network analytics), Heuristic optimisation (genetic algorithm, swarm theory)

- Should be strong at Deep leaning (CNN, LSTM, RNN, Bi-LSTM)

- Must have thorough mathematical knowledge of correlation/causation, decision trees, classification and regression models, recommenders, probability and stochastic processes, distributions, priors and posteriors.

- Skilled at scientific programming languages such as Python, R, Matlab and writing deployable code into production.

- Understand the model lifecycle of cleansing/standardizing raw data, feature creation/selection, writing complex transformation logic to generate independent and dependent variables, model selection, tuning, A/B testing and generating production ready code.

- Knowledge of Numerical optimization, Linear/Non-linear/Integer programming, Statistics, Combinatorial optimization is a plus.

- Familiarity with R, Apache Spark (Scala, Python), PyMC3/theano/tensorflow/Keras and other scientific python/R modules is a must.

- AI skillsets - hands-on Machine learning and Deep Learning algorithms and platforms, neural networks in any, or all the following areas, specifically, in Data & Analytics use cases

- Language - Natural Language Processing, machine translation, emotion detection, language detection, classification

- Vision - computer vision, object recognition/tracking, face/gender/age/emotion recognition, OCR/handwriting recognition

- Knowledge and experience in some of the key AI platforms will be important, e.g. IBM Watson, Microsoft Azure, Google Api.Ai, Facebook Wit.Ai, Chatbots using Microsoft Bot Framework

- Knowledge and experience of key machine learning and deep learning framework, e.g. Keras, TensorFlow, Caffe, CNTK, Jiraffe, MXNet and PyTorch commercial technologies/platforms, etc

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