Senior Data Scientist, Revenue Analytics
J
JobgetherSecurity & IT
Based in the United StatesFull-TimeSenior
Salary$125,000 to $135,000 USD
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLGitData visualizationData modelingA/B testing
Requirements
- 5+ years of experience in Data Science, Analytics, Management Consulting, or a related quantitative field.
- Advanced degree in Data Science, Statistics, Economics, Analytics, Mathematics, Computer Science, or a closely related quantitative discipline.
- Expert-level proficiency in SQL and Python for data analysis, modeling, and automation.
- Strong experience designing and evaluating experiments, including A/B testing, statistical analysis, simulation modeling, and causal inference techniques.
- Experience working with Git and collaborative software development workflows.
- Strong written and verbal communication skills with the ability to explain technical concepts to technical and non-technical stakeholders.
- Demonstrated ability to lead cross-functional projects and influence strategic decisions through data-driven recommendations.
- Passion for applying AI tools and emerging technologies to improve analytics workflows.
- Ability to thrive in fast-paced environments characterized by ambiguity and evolving priorities.
Responsibilities
- Lead complex, cross-functional analytics initiatives that identify revenue opportunities and influence strategic business decisions.
- Develop innovative data-driven strategies to optimize customer acquisition, revenue generation, and overall business performance across the revenue lifecycle.
- Design, execute, and interpret advanced quantitative analyses, including A/B testing, simulation modeling, causal inference, and statistical experimentation.
- Build, maintain, and optimize data pipelines, analytical models, and reporting frameworks to improve data quality and business insights.
- Collaborate with product, engineering, growth, sales, and risk teams to translate business challenges into scalable analytical solutions.
- Present actionable insights and strategic recommendations to senior leadership through clear data storytelling and visualization.
- Champion the adoption of AI technologies and advanced analytical tools to improve team productivity, efficiency, and decision-making.
- Mentor teammates by promoting analytical excellence, best practices, and a culture of continuous learning and innovation.
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