Data Scientist - Account Success

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category
Data

Job Details

About Salesforce

We're Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too - driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good - you've come to the right place.

As a Data Scientist supporting our Account Success team, you will be at the forefront of our efforts to secure and grow our customer relationships. This role is pivotal in proactively identifying risks that could lead to license attrition or declines in consumption and in quantifying the effectiveness of our customer engagement strategies.

You will be responsible for developing sophisticated models that predict customer churn and identify key drivers of risk, such as low product adoption. Furthermore, you will design and execute in-depth impact studies to understand which customer treatments-from support engagements to marketing campaigns-are most effective at improving product adoption and fostering long-term loyalty.

This position requires a dynamic individual who can transform complex datasets into actionable strategies. You will own the analytical lifecycle from data extraction and modeling to delivering insights that directly influence how our Account Success team engages with customers.

Your Impact:

• Develop and deploy predictive models to identify customers at risk of license attrition or consumption decline, focusing on signals from product adoption data and other contributing factors.
• Design and conduct end-to-end impact studies to measure the causal effect of various customer treatments (e.g., success plays, marketing touches, support interactions) on adoption, loyalty, and retention.
• Partner directly with leaders in Account Success to define critical business questions and translate them into robust analytical and modeling frameworks.
• Build and maintain the necessary data pipelines and modeling frameworks, leveraging complex datasets from multiple sources to support your analyses.
• Translate complex model outputs and analytical findings into clear, actionable recommendations for the Account Success team.
• Own the development of scalable analytical tools and dashboards to provide the field with self-service insights into account health and risk.
• Collaborate with data engineering and other teams to ensure the data infrastructure supports advanced risk modeling and impact analysis.
• Champion a data-driven culture within the Account Success organization by demonstrating the power of predictive and causal analytics.

• BS in a quantitative field such as Statistics, Economics, Computer Science, or Applied Mathematics, or equivalent practical experience.
• 3-5+ years of experience in a data science or advanced analytics role.
• Demonstrable experience applying statistical and machine learning methods to real-world business problems.
• High proficiency in Python or R for data manipulation, modeling, and analysis.
• Expert-level SQL skills for querying and transforming large, complex datasets.
• Strong understanding of statistical concepts and modeling techniques relevant to risk and impact analysis (e.g., survival analysis, logistic regression, classification models, causal inference methods).
• Excellent problem-solving skills and the ability to work independently to drive projects from ideation to completion.

• Master's or PhD in a quantitative field.
• Experience in a B2B or SaaS environment, with a focus on customer success, retention, or growth.
• Proven experience developing models for churn prediction, risk scoring, or customer lifetime value.
• Hands-on experience with causal inference techniques (e.g., propensity score matching, stratified sampling, AB testing) to measure program impact.
• Experience using a variety of data science tools and libraries (e.g., scikit-learn, XGBoost, Shapely Values).
• Familiarity with the Salesforce data model and ecosystem.
• Strong communication and presentation skills, with a knack for telling compelling stories with data.
  
  Note: This role is office-flexible, and the expectation is to be in office three days per week (Chicago OR Indianapolis OR Atlanta).