Data Science and Analytics Manager
Corient, Austin, TX, United States
As a Manager in our Data and AI Solutions team, you will be responsible for developing and maintaining enterprise data science and analytics solutions. You will lead a team that focused on delivering valuable and timely insights to key stakeholders across functions and lines of business.
Key Responsibilities
Data Science and Analytics team leadership. Manage data analysts delivering dashboards, recurring reports, and ad-hoc analysis. Establish consistent standards for data science and analytics development and documentation.
Data solutions. Develop and maintain data solutions, such as descriptive, diagnostic, predictive, and prescriptive analytics focused on enabling decision making, optimization, and measurable business impact for key functional and business stakeholders. Develop and maintain dashboards, recurring reports, and analytical outputs.
Data quality assurance. Implement data validations to ensure integrity of data solutions. Ensure that data processes and outputs are robust, insightful, and timely.
Ad-hoc analytics and insights. Promptly address ad-hoc analysis requests with clear, well-structured outputs rooted in accurate data and focused on delivering actionable insights.
Documentation. Maintain clear documentation of data sources, logic, data transformations, lineage, and outputs.
Cross-functional data science and analytics support. Translate stakeholder needs into clear data specifications to meet business requirements. Act as a senior subject matter expert for enterprise analytics and reporting.
Qualifications
4-6+ years of experience in data science or analytics roles.
2-3+ years of experience managing teams and complex data environments.
Background in Financial Services, consulting, or high‑growth technology environments preferred.
Distinguished academic track record in quantitative discipline.
Skills and competencies
Proficiency in SQL, DBT, and Python in a data science context; proficiency in Tableau a strong plus.
Strong command of best practices for data modeling, data transformations, and data quality frameworks to support scalable, enterprise‑grade data products.
Strong analytical, problem‑solving, and critical thinking skills.
Effective communication, both orally and in writing.
Strong documentation and attention to detail.
Ability to work independently and collaboratively in a fast‑paced, dynamic environment, demonstrating strong organization, prioritization, and follow‑through.
#J-18808-Ljbffr
Key Responsibilities
Data Science and Analytics team leadership. Manage data analysts delivering dashboards, recurring reports, and ad-hoc analysis. Establish consistent standards for data science and analytics development and documentation.
Data solutions. Develop and maintain data solutions, such as descriptive, diagnostic, predictive, and prescriptive analytics focused on enabling decision making, optimization, and measurable business impact for key functional and business stakeholders. Develop and maintain dashboards, recurring reports, and analytical outputs.
Data quality assurance. Implement data validations to ensure integrity of data solutions. Ensure that data processes and outputs are robust, insightful, and timely.
Ad-hoc analytics and insights. Promptly address ad-hoc analysis requests with clear, well-structured outputs rooted in accurate data and focused on delivering actionable insights.
Documentation. Maintain clear documentation of data sources, logic, data transformations, lineage, and outputs.
Cross-functional data science and analytics support. Translate stakeholder needs into clear data specifications to meet business requirements. Act as a senior subject matter expert for enterprise analytics and reporting.
Qualifications
4-6+ years of experience in data science or analytics roles.
2-3+ years of experience managing teams and complex data environments.
Background in Financial Services, consulting, or high‑growth technology environments preferred.
Distinguished academic track record in quantitative discipline.
Skills and competencies
Proficiency in SQL, DBT, and Python in a data science context; proficiency in Tableau a strong plus.
Strong command of best practices for data modeling, data transformations, and data quality frameworks to support scalable, enterprise‑grade data products.
Strong analytical, problem‑solving, and critical thinking skills.
Effective communication, both orally and in writing.
Strong documentation and attention to detail.
Ability to work independently and collaboratively in a fast‑paced, dynamic environment, demonstrating strong organization, prioritization, and follow‑through.
#J-18808-Ljbffr