Lead I - Data Science

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