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Lead I - Data Science
Uttar Tola, India
Role description
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.
Skills
Data Science,Artificial Intelligence,Machine Learning
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