Pay range :$55 /hr to $60/hr on w2
Design and implement measurement frameworks for solutions deployed in production environments
Apply statistical inference and causal methods such as A/B testing, propensity score matching, and instrumental variables
Develop and analyze controlled experiments and observational studies to measure product or operational impact
Collaborate with business and technical stakeholders to define KPIs, success metrics, and measurement strategies
Write clean, reproducible code for statistical analysis, experimentation, and reporting
Implement CI/CD practices and maintain code repositories using GitHub Enterprise
Work within enterprise data environments such as Databricks to manage large-scale datasets and analysis pipelines
Strong understanding of hypothesis testing, OLS, GLM, and causal inference techniques
Proficiency in Python and SQL for statistical modeling and data analysis
Experience with libraries such as statsmodels, scikit-learn, DoWhy, and linearmodels
Hands-on experience with A/B testing and experimental design methodologies
Familiarity with enterprise cloud analytics environments such as Databricks
Strong problem-solving ability with a self-starter mindset and ownership mentality
Ability to work independently while collaborating effectively with cross-functional teams
Preferred Qualifications
Experience in retail, inventory management, or operations research domains
Exposure to cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform
If you are passionate about causal inference, experimentation, and building data-driven measurement frameworks, apply today and help shape the future of enterprise analytics.
Summary / Quick Intro
We are seeking a Data Scientist with strong expertise in statistical inference and causal analysis to design and build measurement frameworks for enterprise solutions. This role focuses on designing experiments, applying causal inference techniques, and developing scalable systems to measure business impact. The ideal candidate will combine strong analytical thinking with practical engineering skills to deliver reliable insights and data-driven decision frameworks.
Design and implement measurement frameworks for solutions deployed in production environments
Apply statistical inference and causal methods such as A/B testing, propensity score matching, and instrumental variables
Develop and analyze controlled experiments and observational studies to measure product or operational impact
Collaborate with business and technical stakeholders to define KPIs, success metrics, and measurement strategies
Write clean, reproducible code for statistical analysis, experimentation, and reporting
Implement CI/CD practices and maintain code repositories using GitHub Enterprise
Work within enterprise data environments such as Databricks to manage large-scale datasets and analysis pipelines
Strong understanding of hypothesis testing, OLS, GLM, and causal inference techniques
Proficiency in Python and SQL for statistical modeling and data analysis
Experience with libraries such as statsmodels, scikit-learn, DoWhy, and linearmodels
Hands-on experience with A/B testing and experimental design methodologies
Familiarity with enterprise cloud analytics environments such as Databricks
Strong problem-solving ability with a self-starter mindset and ownership mentality
Ability to work independently while collaborating effectively with cross-functional teams
Preferred Qualifications
Experience in retail, inventory management, or operations research domains
Exposure to cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform
If you are passionate about causal inference, experimentation, and building data-driven measurement frameworks, apply today and help shape the future of enterprise analytics.
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