Infosys

Consultant - Business Consulting (Data Science)

Job Description

Responsibilities: You will need to be well spoken and have an easy time establishing productive long lasting working relationships with a large variety of stakeholders • Take the lead on data pipeline design with strong analytical skills and a keen eye for detail to really understand and tackle the challenges businesses are facing • You will be confronted with a large variety of Data Science tools and other new technologies as well with a wide variety of IT, compliance, security related issues. • Design and develop world-class technology solutions to solve business problems across multiple client engagements. • Collaborate with other teams to understand business requirements, client infrastructure, platforms and overall strategy to ensure seamless transitions. • Work closely with AI and A global team and with our Fortune 500 clients to build world-class solutions and to define AI strategy. • You will possess strong logical structuring and problem-solving skills with expert level understanding of database and have an inherent desire to turn data into actions. • Strong verbal, written and presentation skills • As a Data Scientist, you will need to have strong programming skills and strong understanding of data science and Machine Learning tools. • You will be working on high impact initiatives with other Data Scientists across the globe Educational Requirements Bachelor of Engineering Additional Responsibities: None Technical and Professional Requirements: • Bachelor's or master’s degree/PhD in Statistics, computer science, information technology, or a similar field. • Solid understanding in Statistics, e.g., hypothesis formulation, hypothesis testing, descriptive analysis and data exploration. • Expert level experience in Python or R. • Strong experience in machine learning algorithms like o Linear/Logistic regression, o Random forest, o SVM, o Neural Networks, o Deep Learning – CNN, RNN, LSTM • Exposure to NLP and Computer vision use cases. • In-depth knowledge of neural networks and architectures • Familiarity with the latest DL architectures. • Toolkits/Frameworks/Libraries - Pytorch, TF/Keras, Scikit-Learn, BERT • Experience in Machine Learning Lifecycle – Data Processing, Algorithm Benchmarking, Validation • Exposure to build an AI model from scratch • Exposure to Graph based databases is a plus. • Experience with relational databases.


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