Specialist II - Data Science

Outcomes:

1.       Work with stakeholders throughout the organization to identify opportunities for leveraging data from our customers to make models that can generate business insights
2.       Create new experimental frameworks or build automated tools to collect data
3.       Correlate similar data sets to find actionable results
4.       Build predictive models and machine learning algorithms to analyse large amounts of information to discover trends and patterns.
5.       Mine and analyse data from company databases to drive optimization and improvement of product development marketing techniques business strategies etc
6.       Develop processes and tools to monitor and analyse model performance and data accuracy.
7.       Develop Data Visualization and illustrations on given business problem
8.       Use predictive modelling to increase and optimize customer experiences and other business outcomes.
9.       Coordinate with different functional teams to implement models and monitor outcomes.
10. Set FAST goals and provide feedback on FAST goals of reportees

Measures of Outcomes:

1.       Number of business processes changed due to vital analysis.
2.       Number of Business Intelligent Dashboards developed
3.       Number of productivity standards defined for project
4.       Number of Prediction and Modelling models used
5.       Number of new approaches applied to understand the business trends
6.       Quality of data visualization done to help non-technical stakeholders comprehend easily.
7. Number of mandatory trainings completed

Outputs Expected:

Statistical Techniques:

1. Apply statistical techniques like regression
   properties of distributions
   statistical tests
   etc. to analyse data.

Machine Learning Techniques:

1. Apply machine learning techniques like clustering
   decision tree learning
   artificial neural networks
   etc. to streamline data analysis.

Creating advanced algorithms:

1. Create advanced algorithms and statistics using regression
   simulation
   scenario analysis
   modelling
   etc.

Data Visualization:

1. Visualize and present data for stakeholders using: Periscope
   Business Objects
   D3
   ggplot
   etc.

Management and Strategy:

1. Oversees the activities of analyst personnel and ensures the efficient execution of their duties.

Critical business insights:

1. Mines the business’s database in search of critical business insights and communicates findings to the relevant departments.

Code:

1. Creating efficient and reusable code meant for the improvement
   manipulation
   and analysis of data.

Version Control:

1. Manages project codebase through version control tools e.g. git
   bitbucket etc.

Predictive analytics:

1. Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis

Prescriptive analytics:

1. Attempts to identify what business action to take

Create Reports:

1. Creates reports depicting the trends and behaviours from the analysed data
2. Training end users on new reports and dashboards.

Document:

1. Create documentation for own work as well as perform peer review of documentation of others' work

Manage knowledge:

1. Consume and contribute to project related documents
   share point
   libraries and client universities