ASSOCIATE DATA SCIENTIST

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.