Staff Data Scientist,

Salesforce•San Francisco, CA

1d•$172,500 - $285,800

About The Position

Salesforce is seeking an experienced Staff Data Scientist to join Slack's Go-to-Market (GTM) organization. This role involves partnering with Sales Strategy, Sales Programs, Product, Finance, and Data teams to identify growth opportunities, enhance customer outcomes, and shape GTM strategy through data-driven insights. The position will tackle high-impact business questions using advanced analytics, experimentation, and statistical methods to understand customer behavior, assess strategic initiatives, and optimize GTM performance. A key aspect of the role will be defining analytical frameworks, predictive models, and measurement systems to support AI-powered selling and customer intelligence. Collaboration with Engineers, Business Leaders, and Researchers is essential to identify opportunities, influence strategy, and foster a culture of evidence-based decision-making. Slack values a positive, diverse, and supportive culture, seeking individuals who are curious, inventive, and committed to continuous improvement, embodying traits of being smart, humble, hardworking, and collaborative.

Requirements

6+ years of industry experience applying data science, statistics, or quantitative analysis to solve complex business problems.
Advanced proficiency in SQL and at least one programming language for data science, such as Python, R, or Scala.
Strong foundation in statistics, experimentation, causal inference, predictive modeling, and analytical problem solving.
Experience working with large-scale data technologies such as Spark, Presto, Hive, Hadoop, or similar distributed data platforms.
Proven ability to communicate complex analytical findings clearly to executive and cross-functional audiences.
Experience partnering with Product, Sales, Finance, or other GTM organizations to influence strategic decisions.
Bachelor's degree in Computer Science, Statistics, Economics, Mathematics, Engineering, or another quantitative field, or equivalent practical experience.

Nice To Haves

Master's degree or PhD in Statistics, Computer Science, Economics, Mathematics, Operations Research, or a related quantitative discipline.
Experience supporting Sales, Marketing, Customer Success, Finance, or other Go-to-Market organizations through advanced analytics.
Expertise in experimental design, A/B testing, causal inference, forecasting, or machine learning.
Experience developing production-quality analytical models and partnering with engineering teams to operationalize data science solutions.
Deep understanding of GTM operating models and experience collaborating with Sales Strategy and Sales Programs teams.
Demonstrated ability to independently define ambiguous business problems, develop rigorous analytical approaches, and influence executive-level strategy.
Experience applying AI and machine learning techniques to improve business decision making, operational efficiency, or customer experiences.
Knowledge of workflow orchestration tools like Apache Airflow is highly desirable.

Responsibilities

Partner with GTM leadership to identify strategic opportunities, evaluate business performance, and influence key business decisions.
Apply statistical methods, experimentation, and advanced analytics to understand customer behavior, sales performance, and drivers of business growth.
Develop predictive models, segmentation frameworks, and forecasting methodologies that improve GTM planning and execution.
Design measurement strategies and success metrics to evaluate products, programs, and strategic initiatives.
Conduct deep-dive analyses to identify opportunities for revenue growth, customer adoption, operational efficiency, and field productivity.
Build scalable analytical frameworks that uncover trends, quantify business impact, and support executive decision making.
Partner with Data Engineering to develop trusted datasets and scalable analytical foundations that enable advanced modeling and experimentation.
Collaborate with Product and Sales Strategy to develop intelligence solutions that connect account health, product usage, customer engagement, and seller actions into actionable recommendations.
Translate complex analyses into compelling narratives, executive presentations, dashboards, and written recommendations tailored to both technical and non-technical audiences.
Champion evidence-based decision making by improving access to trusted metrics, analytical methodologies, and strategic insights across the GTM organization.