Data Scientist

Sotalent; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Sotalent is seeking a data-focused analytics partner to work with Sales, Revenue Operations, Finance, and leadership. You will leverage data science, ML, and experiments to uncover insights, optimize go-to-market strategies, and influence strategic decisions.

You will build predictive models, run A/B tests, and deploy scalable analytics solutions to improve customer outcomes and drive growth. Remote work with some travel within the United States is supported.

Qualifications

• 3–5 years of experience in Data Science, Business Analytics, Revenue Analytics, Go-to-Market Analytics, or similar.

Responsibilities

• Serve as an analytics partner to stakeholders for data-driven insights.
• Collaborate with cross-functional teams to address business objectives.
• Conduct exploratory data analysis to identify customer behaviors and growth opportunities.
• Translate business problems into structured analytical frameworks and actionable recommendations.
• Design, validate, and deploy machine learning models for acquisition, retention, and forecasting.
• Develop predictive models using advanced statistical and ML techniques.
• Design experiments including A/B tests to measure impact of initiatives.
• Automate workflows, reporting, and self-service analytics capabilities.
• Ensure trusted business metrics and data quality across reporting.

Skills

Data science

Go-to-market analytics

SQL

Python

Experimentation

Data storytelling

Tools

SQL databases

Python

BI tools

Cloud data warehouses

MLOps

Job description

Work Setting: Remote (Continental U.S.) with occasional travel.

Are you passionate about leveraging data science, machine learning, and advanced analytics to drive business growth and strategic decision-making? Join a data-driven organization where you'll partner closely with Sales, Revenue Operations, Finance, and business leaders to uncover insights, optimize go-to-market strategies, and influence critical business decisions. In this role, you'll combine analytical expertise, experimentation, statistical modeling, and machine learning to identify growth opportunities, improve customer outcomes, and deliver actionable insights at scale.

Key Responsibilities

• Serve as a trusted analytics partner to business stakeholders, providing data-driven insights that support strategic planning and operational decision-making.
• Collaborate closely with cross-functional teams to understand business objectives, identify opportunities, and develop analytical solutions that drive measurable outcomes.
• Conduct exploratory data analysis to identify customer behaviors, performance trends, growth opportunities, and key factors influencing business success.
• Translate complex and ambiguous business challenges into structured analytical frameworks, measurable hypotheses, and actionable recommendations.
• Design, develop, validate, and deploy machine learning models to support customer acquisition, conversion, retention, expansion, and forecasting initiatives.
• Build predictive models using advanced statistical and machine learning techniques such as classification, regression, clustering, and survival analysis.
• Design and evaluate experiments, including A/B tests and quasi-experimental analyses, to measure the impact of business initiatives and product enhancements.
• Develop and automate analytical workflows, reporting processes, and predictive solutions to improve scalability and efficiency.
• Support the creation and maintenance of trusted business metrics, reporting frameworks, and self-service analytics capabilities.
• Develop robust data models and ensure consistency, reliability, and accessibility of business-critical metrics and insights.
• Write high-quality, maintainable SQL and Python code to support analytics, modeling, and data engineering requirements.
• Participate in code reviews and promote analytical best practices, data quality standards, and scalable solution development.
• Monitor model performance, evaluate results, and continuously improve analytical solutions based on business feedback and changing requirements.

What We're Looking For

• 3–5 years of experience in Data Science, Business Analytics, Revenue Analytics, Go-to-Market Analytics, or a similar analytical role.
• Strong ability to translate business questions into analytical approaches and communicate findings to technical and non-technical audiences.
• Experience designing and interpreting experiments, including A/B testing, power analysis, causal inference, and performance measurement.
• Strong understanding of statistical analysis, predictive modeling, and machine learning methodologies.
• Advanced SQL skills and experience working with large-scale datasets in cloud-based data warehouse environments.
• Experience developing dashboards, reports, and visualizations using business intelligence tools.
• Proficiency in Python for data analysis, machine learning, automation, and statistical modeling.
• Experience using data science libraries and frameworks for model development, feature engineering, and performance evaluation.
• Strong problem-solving, analytical thinking, and data storytelling capabilities.
• Ability to work independently while managing multiple priorities in a fast-paced environment.

Preferred Qualifications

• Experience supporting Sales, Revenue Operations, Finance, Marketing, Customer Success, or other go-to-market functions.
• Hands-on experience developing production-ready machine learning models and automated analytics solutions.
• Knowledge of MLOps practices including model deployment, monitoring, retraining, version control, and workflow automation.
• Experience with customer lifecycle analytics, growth analytics, customer retention, churn prediction, and expansion modeling.
• Familiarity with product usage analytics, behavioral data analysis, and customer journey measurement.
• Experience implementing AI-assisted workflows, intelligent automation, or advanced analytics solutions.
• Knowledge of business performance metrics such as revenue growth, recurring revenue, customer retention, customer acquisition, and usage-based metrics.
• Experience working in cloud-based data environments and modern analytics ecosystems.