Business & Marketing Data Scientist (III)- # 26-24533

• Translate complexbusiness and marketing problems into analytical solutions, models, and actionable insights.
• Developstatistical models, evaluation metrics, and data science solutions to support business decision-making.
• Partner withbusiness, product, and engineering teams to evaluate product impact and key metrics such asengagement, retention, and churn.
• Gather, extract, combine, and analyze data from multiple sources usingSQL, Python, R, and related tools.
• Drivedata preparation, restructuring, formatting, validation, and quality checks to ensure datasets are analysis-ready.
• Developreusable and scalable analytical solutions within the team's development processes.
• Providetechnical guidance and mentorship on data science and analytical projects.
• Build stakeholder trust by translating business requirements intotractable analyses, measurable metrics, and clear recommendations.
• Ensure appropriate consideration ofdata privacy, confidentiality, and security throughout data extraction and analysis.

Required Qualifications / Skills

• Strong experience indata science, business analytics, marketing analytics, or a related field.
• StrongSQL skills with experience extracting, joining, transforming, and validating data.
• Hands-on experience withPython and/or R for data analysis and statistical modeling.
• Experience developingstatistical models, analytical frameworks, or evaluation metrics.
• Ability to translate ambiguousbusiness problems into structured analytical approaches.
• Experience working withlarge, complex, or multi-source datasets.
• Strong understanding ofdata quality, validation, and preparation for analysis.
• Experience collaborating withcross-functional business, product, and engineering stakeholders.
• Strong communication skills with the ability to explain technical findings tonon-technical stakeholders.

Nice to Have

• Experience inmarketing analytics, customer analytics, or product analytics.
• Experience analyzingengagement, retention, churn, or customer lifecycle metrics.
• Experience buildingreusable, scalable data science or analytics solutions.
• Experience withdata pipelines and data engineering workflows.
• Experience withmachine learning or advanced statistical modeling.