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:

• 5-10 years’ experience in data science/AI-ML/Generative AI development. • Prior hands-on experience in developing complex AI/ML solutions as an AI/Data Scientist or Engineer, in both proof of concept and production environments • Strong knowledge of Machine Learning, Deep Learning, Generative AI/LLMs, and various use cases. • Ability to apply methods such as predictive analytics, time series analysis, hypothesis testing, classification, clustering, and regression analysis. • Strong background in statistics and probability, including experience with descriptive and inferential statistical analysis. • Proficient in a core programming language such as Advanced Python, JavaScript's/ React/Angular, Scala, Spark • Experience with one or multiple databases like SQL server, Postgres, Click house, Presto • In-depth understanding of Cloud Platforms such as AWS, GCP, Azure, and ML platforms like Sage Maker. • Familiarity with Visualization Tools like Tableau, PowerBI • Experience in discovering use cases, scoping, and delivering complex solution architecture designs to diverse audiences, adapting technical depth as needed. • Understanding of DataOps, MLOps, LLMOps, Observability, DevOps, and SRE concepts. • Master’s or Bachelor’s degree in Data Science, Computer Science, or a relevant field. • Strategic thinker with excellent analytical and problem-solving skills. • Strong communication skills, able to drive results & capable of interacting/collaborating with both business decision-makers and other AI/ML experts/coders.