Senior Data Scientist - Customer Experience

About the role

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The Senior Data Scientist on the Enterprise CX team supports 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.

Responsibilities

• Cross-functional Collaboration & Communication: 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.
• Deep-Dive Analysis: Conduct exploratory data analysis and analytical investigations to diagnose metric shifts and uncover actionable trends in customer behavior.
• Applied Modeling: Develop practical predictive models (e.g., churn or upsell forecasting) that directly inform and optimize Customer Success workflows.
• Impact Measurement: Apply basic causal inference and experimentation methodologies to evaluate the true business impact of Customer Success initiatives and product changes.
• Self-Serve Engineering: 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.
• Operational Excellence: 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.
• Revenue Growth: Utilize deep-dive analysis and pragmatic modeling to assist in monitoring renewals and identify leading indicators of risk and opportunity.
• Analytical Support and Proactive Insights: Evaluate business performance to identify the root causes of metric shifts, providing proactive data-driven insights to stakeholders.
• Ai/LlM-Powered Solutions: 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.
• Customer Success Collaboration: Work directly with stakeholders in the Customer Success team to create data stories that lead to customer retention and upsell opportunities.

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.
• Tech-curious mindset with a willingness to learn new technologies and methodologies to stay at the forefront of data science innovation.

Qualifications

• Experience with data science methodologies and tools.
• Knowledge of customer success strategies and metrics.
• Ability to work independently and as part of a team.
• Strong problem-solving and analytical skills.
• Excellent written and verbal communication skills.
• Ability to manage multiple projects simultaneously.

Skills

• Data analysis and modeling.
• SQL and data pipeline tools.
• Python programming.
• Business intelligence tools.
• Machine learning and statistical methods.
• Customer success strategies.
• Collaboration and communication.
• Problem-solving and analytical skills.
• Technical curiosity and learning agility.

Benefits

Not specified.

Pay

US Zone 3 - 4 $132,000 – $166,000 USD

Schedule

Not specified.