Lead II - Data Science

Independently provides expertise on data analysis techniques using software tools; streamlining business processes and managing team

Outcomes:

1. Managing and designing the reporting environment including data sources security and metadata.
2. Providing technical expertise on data storage structures data mining and data cleansing.
3. Supporting the data warehouse in identifying and revising reporting requirements.
4. Supporting initiatives for data integrity and normalization.
5. Assessing tests and implementing new or upgraded software and assisting with strategic decisions on new systems.
6. Synthesize both quantitative and qualitative data into insights
7. Generating reports from single or multiple systems.
8. Troubleshooting the reporting database environment and reports.
9. Understanding business requirements and translating it into executable steps for the team members.
10. Identify and recommend new ways to streamline business processes
11. Illustrates data graphically and translates complex findings into written text.
12. Locating results to help the clients make better decisions. Get feedback from clients and offer to build solutions based on the feedback.
13. Review the team s deliverables before sending final reports to stakeholders.
14. Support cross-functional teams with data reports and insights on data.
15. Training end users on new reports and dashboards.
16. Set FAST goals and provide feedback on FAST goals of reportees

Measures of Outcomes:

1. Quality - number of review comments on codes written
2. Accountable for data consistency and data quality.
3. Number of medium to large custom application data models designed and implemented
4. Illustrates data graphically and translates complex findings into written text.
5. Number of results located to help clients make informed decisions.
6. Attention to detail and level of accuracy.
7. Number of business processes changed due to vital analysis.
8. Number of Business Intelligent Dashboards developed
9. Number of productivity standards defined for project
10. Manage team members and review the tasks submitted by team members
11. 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

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. 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 like git
   bitbucket. etc.

Create Reports:

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

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

Status Reporting:

1. Report status of tasks assigned
2. Comply to project related reporting standards/process

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 look at the numbers trends and data and come to new conclusions based on the findings.
4. Presentation Skills - reports and oral presentations to client
5. Strong meeting facilitation skills as well as presentation skills.
6. Attention to Detail: Making sure to be vigilant in the analysis to come to correct 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 or Power BI.
6. Proficient in mathematics and calculations.
7. Spreadsheet tools such as Microsoft Excel or Google Sheets
8. DBMS
9. Operating Systems and software platforms
10. Knowledge about customer domain and also sub domain where problem is solved

Job Description: We are looking for an experienced Python Developer with a minimum of eight years of experience in Python and its related web frameworks. Experience with machine learning stacks is a plus. Job Location: Bengaluru, India Responsibilities: Develop, test, and maintain web applications using Python and related frameworks like Flask. Write clean, maintainable, and efficient code. Troubleshoot and debug applications to ensure optimal performance. Participate in code reviews to maintain code quality and share knowledge with the team. Work on integrating machine learning models and algorithms into applications (nice to have). Requirements: Minimum of 5 years of professional experience in Python development. Strong knowledge of web frameworks like Flask. Experience with relational databases such as PostgreSQL, MySQL, or similar. Familiarity with version control systems like Git. Knowledge of software development best practices and design patterns. Excellent problem-solving skills and the ability to work independently as well as in a team. Strong communication skills and the ability to articulate technical concepts to non-technical stakeholders. Proficiency in front-end technologies like HTML, CSS, and JavaScript is a plus. Experience with machine learning stacks such as TensorFlow, Keras, or scikit-learn is a plus.