Role Proficiency:

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

Outcomes:

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

Measures of Outcomes:

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

Management and Strategy:

Oversees the activities of analyst personnel and ensures the efficient execution of their duties.

Critical business insights:

Mines the business’s database in search of critical business insights and communicates findings to the 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 like git
bitbucket. etc.

Create Reports:

Create reports depicting the trends and behaviours from the analysed data

Document:

Create documentation for own work as well as perform peer review 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 to project related reporting standards/process

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

Additional Comments:

UST is looking for a Data Scientist to join our Data Science team and help us leverage the power of machine learning to improve the health and well-being of our members. As a Data Scientist , you will use Google Cloud Platform (GCP) tools and technologies, such as Vertex AI and BigQuery, to build, deploy, and monitor scalable and robust machine learning solutions. You will also explore the potential of Generative AI applications to create new value for the business and the customers. You will work closely with business stakeholders, data engineers, and other data scientists to identify opportunities to apply machine learning and shape solutions that meet business needs. You will also contribute to the data science best practices and foster a culture of innovation and collaboration within the team. If you are passionate about data science, health care, and GCP, we would love to hear from you. Key Responsibilities As a Data Scientist, you will perform the following tasks: • Identify opportunities to apply machine learning and shape solutions that meet business needs. • Ideate, prototype, experiment, and build analytical solutions using GCP tools and technologies, such as Vertex AI and BigQuery. • Develop machine learning models, iterating over model classes to find the best model optimized for the specific target and use case. • Conduct model bias audits and ensure that the models are fair, ethical, and aligned with customer values. • Interpret results, identify limitations, constraints, and assumptions, and communicate findings and recommendations to business stakeholders and senior management. • Deploy, monitor, and maintain machine learning solutions in production, ensuring their reliability, performance, and scalability. • Explore the potential of Generative AI applications, such as natural language generation, image synthesis, and data augmentation, to create new value for the business and the customers. • Stay updated on the latest developments and trends in data science, machine learning, and GCP, and share knowledge and insights with the team. Qualifications To be successful in this role, you will need to have the following qualifications: • Bachelor's degree or higher in Computer Science, Statistics, Mathematics, Engineering, or a related field. • Overall experience should be 7+ years and at least 2 years of experience in data science, machine learning, or a similar role. • Experience in a health care-related field or working with health care data is preferred. • Proficient in Python and SQL, and familiar with other programming languages and tools. • Hands-on experience with GCP tools and technologies, such as Vertex AI, BigQuery, Dataflow, Cloud Storage, and Cloud Functions. • Experience in building, deploying, and monitoring machine learning solutions in production, using GCP or other cloud platforms. • Experience in building Generative AI applications, such as natural language generation, image synthesis, or data augmentation, using GCP or other frameworks. • Strong analytical, problem-solving, and communication skills. • Ability to work independently and collaboratively in a fast-paced and dynamic environment. • Passionate about data science, health care, and GCP.