Senior Data Scientist - Customer Experience

Coursera•Mountain View, CA

9h•$132,000 - $166,000•Remote

About The Position

As a Senior Data Scientist on the Enterprise CX team, you are a versatile problem-solver with a solid foundation in end-to-end data science methods. You excel in extracting actionable insights from data to drive strategic decisions and enhance revenue growth. Your expertise lies in conducting deep-dive analyses, diagnosing metric shifts, and applying practical statistical or machine learning methods to solve complex business problems. You are comfortable self-serving across the data stack when needed, and are eager to work collaboratively with stakeholders to deliver impactful solutions that drive business success. The Senior Data Scientist plays a crucial role in supporting the Customer Success team through deep-dive data analysis, diagnostic investigations, targeted predictive modeling, and applied causal inference. This position involves working closely with cross-functional teams to drive revenue growth, reduce customer churn, and enhance operational efficiency. Reporting directly to the Manager of Data Science, you will contribute to the development of end-to-end analytical solutions and measure their true business impact.

Requirements

Bachelor’s degree or higher in a related field, with a focus on data science, statistics, or a related quantitative discipline.
3-5 years of relevant experience in data science, with a demonstrated ability to conduct deep-dive analyses, diagnose metric shifts, and apply pragmatic modeling techniques to drive business impact.
Proficiency in applied statistics and practical machine learning, with knowledge of causal inference, experimentation (A/B testing), forecasting, and regression.
Advanced proficiency in SQL for complex data extraction and manipulation, alongside a working knowledge of data pipelining tools (e.g., dbt, Airflow) to self-serve when necessary.
Proficiency in programming languages such as Python for data analysis, automation, and modeling.
Working knowledge of Business Intelligence tools (e.g., Tableau, Sigma), with a strong understanding of best practices for dashboarding and data visualization to communicate insights.
Hands-on experience designing and deploying AI/LLM-based solutions.
Strong communication skills, with the ability to convey complex concepts clearly and effectively to stakeholders.
Strong organizational skills, with the ability to manage multiple projects and deadlines effectively.
A tech-curious mindset with a willingness to learn new technologies and methodologies to stay at the forefront of data science innovation.

Responsibilities

Collaborate with cross-functional stakeholders, developing a deep business understanding and supporting synergy across the organization.
Communicate effectively with non-technical stakeholders.
Partner closely with the Customer Success team to provide data-driven insights and support decision-making processes.
Conduct exploratory data analysis and analytical investigations to diagnose metric shifts and uncover actionable trends in customer behavior.
Develop practical predictive models (e.g., churn or upsell forecasting) that directly inform and optimize Customer Success workflows.
Apply basic causal inference and experimentation methodologies to evaluate the true business impact of Customer Success initiatives and product changes.
Build and modify foundational data pipelines and simple dashboards when needed to unblock analyses, partnering with core Data Engineering and BI teams for scalable infrastructure.
Optimize data workflows and contribute to data quality, stepping in to self-serve data extraction and transformation tasks when necessary.
Contribute to the establishment and maintenance of Key Performance Indicators (KPIs) for customer success, leveraging descriptive and diagnostic analytics to drive actionable insights.
Utilize deep-dive analysis and pragmatic modeling to assist in monitoring renewals and identify leading indicators of risk and opportunity.
Support ongoing analysis of customer retention, churn, and revenue trends, leveraging both foundational analytics and statistical methods to identify opportunities for growth.
Evaluate business performance to identify the root causes of metric shifts, providing proactive data-driven insights to stakeholders.
Assist in making recommendations to improve business productivity and performance, selecting the right analytical tool—from simple SQL aggregations to statistical modeling—to mitigate risks.
Develop AI/LLM-powered solutions to support CS stakeholders.
Work directly with stakeholders in the Customer Success team to create data stories that lead to customer retention and upsell opportunities.