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.