Senior Data Scientist

● Design and Develop analytical insights based on unstructured and structured data for industry-scale
analytical solutions on Cloud and on Prem.

● Support Junior Data Scientists through their challenges.

● Convert requirements into actual working modules.

● Work with Data Scientists and Distinguished Engineers and Architects to solve problems by using analytics as a tool.

● Responsible for Creating MVPs and Demos for different clients in different domains

Requirements

● You will be highly hands-on and enjoy keeping up with the latest innovations. With a very good grasp of a broad set of machine learning algorithms and software engineering skills, you will have a machine learning / mathematical / statistical background.

● As a Data scientist you will apply your deep analytics expertise, designing and implementing end to end unstructured data mining of large scale big data platforms. Apply semantic correlation, ontology, and text analytics techniques to analyze un-structured data and identify critical insights.

● You will participate in feature / design discussions / workshop with Architect and the offering Management Teams to come up with innovative solutions that can scale.

● What you know:

● Defining the business problem and working hypothesis

● Helping to locate/resolve data/quality issues

● Proficient in the following: Python, SQL.

● Proficient at consuming and building REST APIs

● Proficient at integrating predictive/prescriptive models into applications and processes

● Training and experience applying probability and statistics

● Experience in data modeling and evaluation and a deep understanding of supervised and
unsupervised machine learning

● Experience applying mathematical modeling and/or constraint programming to a range of
industry problems

● Ability to apply predictive models as input into decision optimization problems

● Experience building Monte Carlo simulation/optimization for what-if scenario analysis

● Experience integrating data and the output of predictive and prescriptive models within the
context of a business problem

● Proficiency with data parsing, scraping, and wrangling

● Software life cycle, from analysis, design development, to unit testing, production
deployment and support, CI/CD.