Target Setting and Data Modeling Manager
Salesforce
Target Setting and Data Modeling Manager
Job Description
Description Role OverviewWe are seeking a Manager of Target Setting within our Customer Success organization to manage and execute the framework for setting customer-centric goals. This is a critical role responsible for translating Salesforce's strategic objectives into meaningful, measurable targets. You will partner with stakeholders across Customer Success, Product, and Operations to build world-class goal-setting models that promotes customer health and long-term value. Success in this role requires a deep expertise in statistical modeling, data analytics, and SaaS metrics.Key ResponsibilitiesDesign, build, and maintain the core analytical models that inform goal setting, using factors like customer segmentation, product usage data, and health scores.Develop and manage forecasting models to predict future performance against key goals, running deep-dive analyses to identify opportunities and mitigate risks.Manage the end-to-end technical process for setting and tracking Customer Success performance targets, ensuring data integrity and model accuracy.Monitor performance against goals and translate complex model outputs into actionable narratives and dashboards for senior leadership.Partner with Data Science, Product, and Analytics teams to integrate new data sources and continuously improve the technical framework of the goal-setting process.Qualifications & Experience6+ years in a highly analytical role such as Data Analytics, Business Operations, or Data Science, with a track record of developing performance frameworks and predictive models.Expert-level proficiency in SQL for complex data extraction and manipulation is required. Hands-on experience building statistical and forecasting models in Python or R. Mastery of a data visualization tool like Tableau.Proven, hands-on experience developing predictive models from the ground up, with a strong understanding of statistical methods, machine learning concepts (e.g., regression, clustering), and time-series forecasting.Experience querying and working with large-scale datasets from enterprise data warehouses and CRM systems (Salesforce).Exceptional ability to translate complex quantitative analysis into clear, actionable insights and strategic recommendations for technical and non-technical audiences.Strong business acumen with a deep understanding of SaaS business models, customer lifecycle management, and how to connect operational KPIs to financial outcomes.Proven ability to manage complex projects, thrive in a fast-paced environment, and manage multiple priorities simultaneously.Excellent communication and interpersonal skills, with the ability to influence and collaborate effectively with stakeholders at all levels of the organization.High comfort with ambiguity and a proactive, problem-solving mindset.
Locations
- Indianapolis, Indiana
- Seattle, Washington
- Atlanta, Georgia
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
140,000 - 200,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- proficiency in SQLintermediate
- experience building statistical and forecasting models in Python or Rintermediate
- Mastery of a data visualization tool like Tableauintermediate
- experience developing predictive modelsintermediate
- understanding of statistical methodsintermediate
- machine learning concepts (e.g., regression, clustering)intermediate
- time-series forecastingintermediate
- querying and working with large-scale datasets from enterprise data warehouses and CRM systems (Salesforce)intermediate
Required Qualifications
- 6+ years in a highly analytical role such as Data Analytics, Business Operations, or Data Science, with a track record of developing performance frameworks and predictive models. (experience, 6 years)
- Expert-level proficiency in SQL for complex data extraction and manipulation is required. (experience)
- Hands-on experience building statistical and forecasting models in Python or R. (experience)
- Mastery of a data visualization tool like Tableau. (degree)
- Proven, hands-on experience developing predictive models from the ground up, with a strong understanding of statistical methods, machine learning concepts (e.g., regression, clustering), and time-series forecasting. (experience)
- Experience querying and working with large-scale datasets from enterprise data warehouses and CRM systems (Salesforce). (experience)
- Exceptional ability to translate complex quantitative analysis into clear, actionable insights and strategic recommendations for technical and non-technical audiences. (experience)
- Strong business acumen with a deep understanding of SaaS business models, customer lifecycle management, and how to connect operational KPIs to financial outcomes. (experience)
- Proven ability to manage complex projects, thrive in a fast-paced environment, and manage multiple priorities simultaneously. (experience)
- Excellent communication and interpersonal skills, with the ability to influence and collaborate effectively with stakeholders at all levels of the organization. (experience)
- High comfort with ambiguity and a proactive, problem-solving mindset. (experience)
Responsibilities
- Design, build, and maintain the core analytical models that inform goal setting, using factors like customer segmentation, product usage data, and health scores.
- Develop and manage forecasting models to predict future performance against key goals, running deep-dive analyses to identify opportunities and mitigate risks.
- Manage the end-to-end technical process for setting and tracking Customer Success performance targets, ensuring data integrity and model accuracy.
- Monitor performance against goals and translate complex model outputs into actionable narratives and dashboards for senior leadership.
- Partner with Data Science, Product, and Analytics teams to integrate new data sources and continuously improve the technical framework of the goal-setting process.
Tags & Categories
Target Setting and Data Modeling Manager
Salesforce
Target Setting and Data Modeling Manager
Job Description
Description Role OverviewWe are seeking a Manager of Target Setting within our Customer Success organization to manage and execute the framework for setting customer-centric goals. This is a critical role responsible for translating Salesforce's strategic objectives into meaningful, measurable targets. You will partner with stakeholders across Customer Success, Product, and Operations to build world-class goal-setting models that promotes customer health and long-term value. Success in this role requires a deep expertise in statistical modeling, data analytics, and SaaS metrics.Key ResponsibilitiesDesign, build, and maintain the core analytical models that inform goal setting, using factors like customer segmentation, product usage data, and health scores.Develop and manage forecasting models to predict future performance against key goals, running deep-dive analyses to identify opportunities and mitigate risks.Manage the end-to-end technical process for setting and tracking Customer Success performance targets, ensuring data integrity and model accuracy.Monitor performance against goals and translate complex model outputs into actionable narratives and dashboards for senior leadership.Partner with Data Science, Product, and Analytics teams to integrate new data sources and continuously improve the technical framework of the goal-setting process.Qualifications & Experience6+ years in a highly analytical role such as Data Analytics, Business Operations, or Data Science, with a track record of developing performance frameworks and predictive models.Expert-level proficiency in SQL for complex data extraction and manipulation is required. Hands-on experience building statistical and forecasting models in Python or R. Mastery of a data visualization tool like Tableau.Proven, hands-on experience developing predictive models from the ground up, with a strong understanding of statistical methods, machine learning concepts (e.g., regression, clustering), and time-series forecasting.Experience querying and working with large-scale datasets from enterprise data warehouses and CRM systems (Salesforce).Exceptional ability to translate complex quantitative analysis into clear, actionable insights and strategic recommendations for technical and non-technical audiences.Strong business acumen with a deep understanding of SaaS business models, customer lifecycle management, and how to connect operational KPIs to financial outcomes.Proven ability to manage complex projects, thrive in a fast-paced environment, and manage multiple priorities simultaneously.Excellent communication and interpersonal skills, with the ability to influence and collaborate effectively with stakeholders at all levels of the organization.High comfort with ambiguity and a proactive, problem-solving mindset.
Locations
- Indianapolis, Indiana
- Seattle, Washington
- Atlanta, Georgia
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
140,000 - 200,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- proficiency in SQLintermediate
- experience building statistical and forecasting models in Python or Rintermediate
- Mastery of a data visualization tool like Tableauintermediate
- experience developing predictive modelsintermediate
- understanding of statistical methodsintermediate
- machine learning concepts (e.g., regression, clustering)intermediate
- time-series forecastingintermediate
- querying and working with large-scale datasets from enterprise data warehouses and CRM systems (Salesforce)intermediate
Required Qualifications
- 6+ years in a highly analytical role such as Data Analytics, Business Operations, or Data Science, with a track record of developing performance frameworks and predictive models. (experience, 6 years)
- Expert-level proficiency in SQL for complex data extraction and manipulation is required. (experience)
- Hands-on experience building statistical and forecasting models in Python or R. (experience)
- Mastery of a data visualization tool like Tableau. (degree)
- Proven, hands-on experience developing predictive models from the ground up, with a strong understanding of statistical methods, machine learning concepts (e.g., regression, clustering), and time-series forecasting. (experience)
- Experience querying and working with large-scale datasets from enterprise data warehouses and CRM systems (Salesforce). (experience)
- Exceptional ability to translate complex quantitative analysis into clear, actionable insights and strategic recommendations for technical and non-technical audiences. (experience)
- Strong business acumen with a deep understanding of SaaS business models, customer lifecycle management, and how to connect operational KPIs to financial outcomes. (experience)
- Proven ability to manage complex projects, thrive in a fast-paced environment, and manage multiple priorities simultaneously. (experience)
- Excellent communication and interpersonal skills, with the ability to influence and collaborate effectively with stakeholders at all levels of the organization. (experience)
- High comfort with ambiguity and a proactive, problem-solving mindset. (experience)
Responsibilities
- Design, build, and maintain the core analytical models that inform goal setting, using factors like customer segmentation, product usage data, and health scores.
- Develop and manage forecasting models to predict future performance against key goals, running deep-dive analyses to identify opportunities and mitigate risks.
- Manage the end-to-end technical process for setting and tracking Customer Success performance targets, ensuring data integrity and model accuracy.
- Monitor performance against goals and translate complex model outputs into actionable narratives and dashboards for senior leadership.
- Partner with Data Science, Product, and Analytics teams to integrate new data sources and continuously improve the technical framework of the goal-setting process.
Tags & Categories