Role Proficiency:

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

Outcomes:

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

Measures of Outcomes:

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

Outputs Expected:

Determine Specific Data needs:

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

Critical business insights:

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

Code:

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

Create/Validate Data Models:

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

Predictive analytics:

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

Prescriptive analytics:

Attempts to identify what business action to take

Code Versioning:

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

Create Reports:

Create reports depicting the trends and behaviours from analyzed data

Document:

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

Manage knowledge:

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

Status Reporting:

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

Skill Examples:

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

Knowledge Examples:

Knowledge Examples

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

Additional Comments:

Data Science (Core AI ML) Masters in AI ML / Computer Science Background AI ML Models, Pyspark, Tableau, Sql, Power BI Demonstrates in- depth level abilities and/or a proven record of success managing efforts with identifying and addressing client needs: • As a critical member of a team of the data science team, you will maintain and analyze large, complex datasets to uncover insights that inform topics across one of the 3 Industries (Industrial Products, Financial Services and Technology, Media and Telecom). • Support in the identification of new, cutting-edge datasets that add to the firm's differentiation amongst competitors and clients; • Support in building predictive models and data-led tools; • Design and conduct experiments (A/B testing, market basket analysis, etc.) to measure the effectiveness of new approaches and drive continuous improvement; • Partner with US team to translate analytical findings into actionable recommendations and compelling stories; • Develop dashboards and reports using tools like Tableau, Power BI, or Looker to support self-service analytics and decision-making; • Stay up to date and ahead of industry trends, customer behavior patterns, and emerging technologies in the aligned sector. • Experience managing high performing data science and commercial analytics teams; • Strong SQL and Alteryx skills and proficiency in Python and/or R for data manipulation and modeling; • Experience applying machine learning or statistical techniques to real-world business problems; • Solid understanding of key Industry specific metrics • Proven ability to explain complex data concepts to non-technical stakeholders; • Experience with Industry specific datasets and vendors • Knowledge of geospatial or time-series analysis and, • Prior work with commercial analytics and insights