Not Applicable

Qualification:

Post-grad in one of the following fields with strong academic credentials:

• Computer Science/IT. • Operations Research/Applied Math. • Engineering. • Statistics.

Responsibility:

Business:

'Works with the business team to identify the right business objective and data required to answer the same. Executes a data collection plan from both structured and unstructured sources that helps in data exploration , hypothesis testing and statistical modeling. Analyzes data and generates insights that can articulated to business stakeholders. Develops POCs that can be used to generate business decisions. Simulates several scenarios through an interactive visualization that deepens the business understanding with underlying trends and root causes.

Stakeholder Management:

'• Communicate analytical results in a way that is meaningful for business stakeholders and provides actionable insights. • Coordinates in communicating the data needs with both technology and business teams to ensure that right data is captured for analysis and modeling.

Project Management:

'• Ensure that the work goes ahead in a way that conforms to DS delivery framewrok and fulfills all outputs specifically outlined by stakeholders. • Work with Business Operations and Delivering teams on designing, building and deploying data analysis systems for large data sets. • Execute flawlessly on the DS project plan by adhering to timelines. • Execute the design, analysis, or evaluation of assigned projects.

Data Analytics and Reporting:

'• Explore and examine data from multiple disparate sources. Prepare a data collection plan from both structured and unstructured sources. Collaborate and coordinate with Technology and Business teams for all data needs. • Expert level proficiency in data handling (SQL). Data Discovery & Profiling.

Data Discovery & Profiling:

'• Perform exploratory data analysis and generate insights. Validate hypothesis developed during exploration phase. Present initial results to business stakeholders and identify the next steps.

Data Modelling:

Create models using one or more of the platforms like R, SAS, Python, Matlab Model creation would involve one or more of the following technqiues:

1 Classification. 2 Clusterning. 3 Time Series. 4 Forecasting. '• Testing and validating the model. • Deriving insights and recommendations from the models. • Performing data visualization and presentation to clients.

Innovation & Thought Leadership:

'• Provide thoughtleadership and dependable execution on diverse projects. • implement best practices and technology. • Discover new opportunities where advanced analytical techniques can be leveraged for solving business problems.

Knowledge Management:

Document all modeling steps in a systematic way including modeling process, insights generated , presentations , model validation results and checklists built in the project. Prepare a one pager document that outlines and quantifies the business impact due to the DS project.

Must Have Skills

BigQuerySQL DeveloperItem Management