Home Jobs Data Scientist
Back to Jobs

Data Scientist - Job Opportunity at GoTo

Remote, US
Full-time
Mid-level
Posted: July 17, 2025
Remote
USD 99,000 - 164,000 per year

Benefits

Comprehensive health coverage including medical, dental, and vision insurance providing complete healthcare security
401(k) retirement plan with discretionary company matching to accelerate wealth building
Generous paid time off policy with additional quarterly self-care days for enhanced work-life balance
Life and disability insurance coverage ensuring financial protection for employees and families
Fertility and family-forming support programs demonstrating progressive employee care
Employee assistance program offering confidential support for personal and professional challenges
Tuition and reading reimbursement programs supporting continuous professional development
Thrive Global Wellness Program with one-to-one coaching for holistic employee wellbeing
Employee Resource Groups fostering inclusive workplace community and networking
Volunteer time off enabling community engagement while maintaining salary
Paid holidays providing standard time off for rest and family time
Employee discount programs offering financial savings on products and services
Charitable matching program amplifying personal giving impact

Key Responsibilities

Architect and deploy machine learning models that directly impact customer retention KPIs, driving measurable business value through predictive analytics
Build and maintain production-grade data pipelines ensuring scalable model deployment and continuous performance optimization
Lead cross-functional collaboration with Marketing, Customer Success, Sales, and Product teams to translate data insights into strategic retention initiatives
Transform complex quantitative analysis into actionable business intelligence for executive decision-making
Drive data-driven process improvements across multiple departments by understanding operational requirements and delivering targeted analytical solutions
Establish robust model monitoring and enhancement protocols to ensure sustained predictive accuracy in production environments

Requirements

Education

Bachelor's degree (Master's preferred) in a quantitative field - Statistics, Computer Science, Math, Physics etc.

Experience

At least 2 years of experience in an analytical and/or data science role within a business environment, or a similar duration of experience from a PhD research program

Required Skills

SQL Python PySpark Big Data and Cloud systems (e.g. AWS, Databricks, S3, EMR, Hive, Presto) Git for version control and code management Fundamentals of statistics Machine learning algorithms and their applications Excellent communication skills Intellectual curiosity with desire to learn and grow
Advertisement
Ad Space

Sauge AI Market Intelligence

Industry Trends

Customer retention analytics has become increasingly critical as businesses face rising customer acquisition costs, with companies investing heavily in predictive modeling to identify at-risk customers before churn occurs. The shift toward subscription-based business models across industries has made retention data science a strategic imperative rather than a nice-to-have capability. The integration of real-time machine learning in customer success platforms is accelerating, driven by the need for immediate intervention capabilities when customer health scores decline. Organizations are moving beyond traditional batch processing to implement streaming analytics that can trigger automated retention campaigns. Cloud-native data science platforms are becoming the standard for retention analytics, with AWS, Databricks, and similar technologies enabling scalable model deployment and real-time scoring. This technological shift is democratizing advanced analytics capabilities for mid-market companies previously limited to basic reporting. Cross-functional data science collaboration is intensifying as organizations recognize that effective retention strategies require deep integration between technical teams and business stakeholders. The most successful retention programs now feature embedded data scientists working directly with customer success, marketing, and product teams.

Salary Evaluation

The offered salary range of $99,000-$164,000 is competitive for a mid-level data scientist role in the current market, particularly for a remote position. This range aligns well with industry standards for retention-focused data science roles, especially considering the specialized nature of customer health prediction modeling and the company's established market presence in the collaboration software space.

Role Significance

The role appears to be part of a small, collaborative Retention Data Science Team, likely consisting of 3-5 data scientists and analysts working closely with a broader customer success organization. This team structure suggests opportunities for mentorship, knowledge sharing, and direct impact on team direction and methodology.
This is a mid-level individual contributor role with significant strategic impact, positioned to influence customer retention initiatives across multiple business functions. The role carries substantial responsibility for production model performance and cross-functional collaboration, indicating a position of trust and influence within the organization's data science capability.

Key Projects

Development and deployment of customer health scoring models that predict churn risk across different customer segments and product lines Implementation of real-time monitoring systems that alert customer success teams to declining customer health indicators Creation of attribution models that identify which customer interactions and product usage patterns most strongly correlate with retention outcomes Design of automated customer segmentation systems that enable personalized retention strategies based on predicted customer lifetime value

Success Factors

Ability to translate complex statistical concepts into actionable business insights that non-technical stakeholders can understand and implement effectively Strong foundation in production machine learning engineering, including model versioning, monitoring, and automated retraining pipelines Collaborative mindset that enables effective partnership with customer success, marketing, and product teams to understand business context and requirements Proficiency in cloud-based data science platforms and big data technologies that enable scalable model deployment and real-time scoring Intellectual curiosity and continuous learning orientation that drives exploration of new modeling techniques and industry best practices

Market Demand

High - Customer retention data science roles are experiencing strong demand as companies prioritize reducing churn in an increasingly competitive market. The specific focus on customer health prediction modeling represents a high-value specialization that commands premium compensation and offers excellent job security.

Important Skills

Critical Skills

Python and SQL proficiency are fundamental for data manipulation, analysis, and model development in the customer retention context. These skills enable the core technical work of extracting insights from customer behavior data and building predictive models. Machine learning algorithm understanding is essential for selecting appropriate modeling techniques for customer health prediction, including classification models for churn prediction and regression models for customer lifetime value estimation. Cloud platform expertise (AWS, Databricks) is crucial for deploying and scaling retention models in production environments, enabling real-time customer health scoring and automated intervention triggers. Communication skills are vital for translating complex analytical findings into actionable retention strategies that customer success and marketing teams can implement effectively.

Beneficial Skills

Advanced statistical modeling techniques including survival analysis and time-series forecasting would enhance capability to predict customer behavior over time A/B testing and experimentation design skills would enable more sophisticated evaluation of retention intervention strategies Customer success domain knowledge would accelerate understanding of business context and improve model relevance MLOps and model governance expertise would support more sophisticated production deployment and monitoring capabilities

Unique Aspects

Focus on customer health prediction modeling provides exposure to cutting-edge retention analytics techniques and their direct business impact
Small team environment offers opportunities for significant individual contribution and rapid skill development across multiple aspects of the data science lifecycle
Direct collaboration with customer-facing teams provides valuable business context and accelerates understanding of how data science drives commercial outcomes
Emphasis on continuous improvement and technical feedback creates a learning environment that encourages experimentation and professional growth

Career Growth

Career progression to senior-level roles typically occurs within 2-3 years given the strategic nature of retention analytics and the high-impact visibility of customer health prediction models. The cross-functional collaboration aspect of this role accelerates professional development by building business acumen alongside technical expertise.

Potential Next Roles

Senior Data Scientist specializing in customer analytics and retention modeling Lead Data Scientist with team management responsibilities and strategic planning involvement Customer Success Analytics Manager bridging technical and business functions Principal Data Scientist focusing on advanced machine learning research and methodology development

Company Overview

GoTo

GoTo is an established player in the collaboration and IT management software space, known for products like GoToMeeting and LogMeIn. The company has undergone significant transformation in recent years, positioning itself as a comprehensive business communications platform serving millions of users globally.

GoTo operates in the competitive unified communications and collaboration market, competing with companies like Zoom, Microsoft Teams, and Slack. The company maintains a strong position in the mid-market and enterprise segments, particularly in IT management and remote access solutions.
As a US-based company offering remote work arrangements, GoTo provides access to a broad talent pool while maintaining competitive compensation standards. The remote-first approach reflects the company's alignment with modern work practices and its own collaboration technology solutions.
The company emphasizes simplification and automation as core values, creating an environment where data scientists can focus on impactful work rather than bureaucratic processes. The mention of continuous improvement through open technical feedback suggests a learning-oriented culture that values professional growth and innovation.
Advertisement
Ad Space
Apply Now

Data Sources & Analysis Information

Job Listings Data

The job listings displayed on this platform are sourced through BrightData's comprehensive API, ensuring up-to-date and accurate job market information.

Sauge AI Market Intelligence

Our advanced AI system analyzes each job listing to provide valuable insights including:

  • Industry trends and market dynamics
  • Salary estimates and market demand analysis
  • Role significance and career growth potential
  • Critical success factors and key skills
  • Unique aspects of each position

This integration of reliable job data with AI-powered analysis helps provide you with comprehensive insights for making informed career decisions.