Lead I - Data Science

Provide expertise on data analysis techniques using software tools. Under supervision streamline business processes.

Outcomes:

1. Design and manage the reporting environment; which include data sources security and metadata.
2. Provide technical expertise on data storage structures data mining and data cleansing.
3. Support the data warehouse in identifying and revising reporting requirements.
4. Support initiatives for data integrity and normalization.
5. Assess tests and implement new or upgraded software. Assist with strategic decisions on new systems. Generate reports from single or multiple systems.
6. Troubleshoot the reporting database environment and associated reports.
7. Identify and recommend new ways to streamline business processes
8. Illustrate data graphically and translate complex findings into written text.
9. Locate results to help clients make better decisions. Solicit feedback from clients and build solutions based on feedback.
10. Train end users on new reports and dashboards.
11. Set FAST goals and provide feedback on FAST goals of repartees

Measures of Outcomes:

1. Quality - number of review comments on codes written
2. Data consistency and data quality.
3. Number of medium to large custom application data models designed and implemented
4. Illustrates data graphically; translates complex findings into written text.
5. Number of results located to help clients make informed decisions.
6. Number of business processes changed due to vital analysis.
7. Number of Business Intelligent Dashboards developed
8. Number of productivity standards defined for project
9. Number of mandatory trainings completed

Outputs Expected:

Determine Specific Data needs:

1. Work with departmental managers to outline the specific data needs for each business method analysis project

Critical business insights:

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

Code:

1. Creates efficient and reusable SQL code meant for the improvement
   manipulation
   and analysis of data.
2. Creates efficient and reusable code. Follows coding best practices.

Create/Validate Data Models:

1. Builds statistical models; diagnoses
   validates
   and improves the performance of these models over time.

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

Code Versioning:

1. Organize and manage the changes and revisions to code. Use a version control tool for example git
   bitbucket. etc.

Create Reports:

1. Create reports depicting the trends and behaviours from analyzed data

Document:

1. Create documentation for worked performed. Additionally
   perform peer reviews of documentation of others work

Manage knowledge:

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

Status Reporting:

1. Report status of tasks assigned
2. Comply with project related reporting standards and processes

Skill Examples:

1. Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
2. Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.
3. Critical Thinking: Data Analysts must review numbers trends and data to come up with original conclusions based on the findings.
4. Presentation Skills - facilitates reports and oral presentations to senior colleagues
5. Strong meeting facilitation skills as well as presentation skills.
6. Attention to Detail: Vigilant in the analysis to determine accurate conclusions.
7. Mathematical Skills to estimate numerical data.
8. Work in a team environment
9. Proactively ask for and offer help

Knowledge Examples:

Knowledge Examples

1. Database languages such as SQL
2. Programming language such as R or Python
3. Analytical tools and languages such as SAS & Mahout.
4. Proficiency in MATLAB.
5. Data visualization software such as Tableau or Qlik.
6. Proficient in mathematics and calculations.
7. Efficiently with spreadsheet tools such as Microsoft Excel or Google Sheets
8. DBMS
9. Operating Systems and software platforms
10. Knowledge regarding customer domain and sub domain where problem is solved

JD I stated some specifics around Collibra. They may not know all areas and could be specialized in one area. I would still look at their profile. The skill set required includes: Analysis skillset Understand database structures Understand source to target mapping Proficient in SQL Queries Proficient in Excel Collibra Data Intelligence Platform (DIP) and/or Collibra Data Quality (DQ) experience Here are some experience areas within Collibra that would be helpful: Reference Data Management experience within Collibra DIP API Framework experience within Collibra DIP Catalog asset synchronization experience within Collibra DIP Technical Lineage and Dataset experience within Collibra DIP Business Glossary, Catalog and Global Views usage experience within Collibra DIP Data Profiling and Rule Discovery experience within Collibra DQ