Data Scientist ( Statistical Inference and Causal Analysis )

About The Role We are looking for a Data Scientist with strong expertise in statistical inference and causal analysis to develop measurement frameworks for enterprise solutions. This role is ideal for someone passionate about designing experiments, applying causal inference methods, and building scalable frameworks to measure impact. Key Responsibilities • Design and implement measurement frameworks for solutions in production. • Apply statistical inference and causal methods (e.g., A/B testing, propensity score matching, instrumental variables). • Develop and analyze controlled experiments and observational studies. • Collaborate with stakeholders to define KPIs and measurement strategies. • Write clean, reproducible code for statistical analysis and reporting. • Implement CI/CD principles and manage code repositories using GitHub Enterprise. Required Qualifications • Strong knowledge of hypothesis testing, OLS, GLM, and causal inference techniques. • Proficiency in Python and SQL; experience with libraries like statsmodels, scikit-learn, DoWhy, linearmodels. • Experience with A/B testing and experimental design. • Familiarity with Databricks or similar enterprise cloud environments. • Self-starter with an ownership mindset and ability to work independently. Preferred Qualifications • Experience in retail, inventory management, or operations research. • Exposure to cloud platforms (Azure, AWS, GCP). Horizontal facilitates valuable and productive conversations between you and potential employers. We can assist you in growing your career by partnering you with employers that offer challenging assignments. For those that join the team, we offer competitive compensation and benefits including medical, dental, vision, and retirement. Check out all we have to offer and how you can become part of the Horizontal Talent Team. The pay range for this role is $30 - $62 per hour. This is not a guarantee of compensation, as final offer amount may vary based on factors including but not limited to experience and geographic location.