Research business domain and develop use-cases to support enterprise wide AI solutions.

Perform high value data interpretation with quality, action-oriented results to transform business

Model design, feature planning, system infrastructure, production setup and monitoring, and release management.

Watch external industry, research and analysis to give insight to markets on industry trends and sales targets

Excellent understanding of machine learning techniques and algorithms, such as SVM, Decision Forests, k-NN, Naive Bayes etc.

Experience in selecting features, building and optimizing classifiers using machine learning techniques.

Prior experience with data visualization tools, such as D3.js, GGplot, etc..

Good knowledge on statistics skills, such as distributions, statistical testing, regression, etc..

Adequate presentation and communication skills to explain results and methodologies to non-technical stakeholders.

Basic understanding of the banking industry is value add.

Minimum 8+ years of experience in designing/developing industrial IT solutions.

Minimum 6 years of experience in developing and deploying machine learning, deep learning, NLP solutions.

Extensive programming experience with Python

Very strong grasp of IT concepts with a strong algorithms/data structures background.

Demonstrated history of building prototypes to win business confidence.

Background building systems with a major machine learning library such as Keras

Background in applying deep learning to real problems and knowledge of a major framework

Experience with common data science toolkits, such as Scikit, NumPy, R libraries – Excellence in at least one of these is mandatory.

Proficiency with any one NoSQL databases such as MongoDB, Cassandra, HBase.

Should have prior experience in developing APIs, services using either C# or Java.

Should have good awareness on entire machine learning/ predictive modeling & implementation.