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.