Data Scientist – Causal Inference and Statistical Analysis

Title: Data Scientist – Causal Inference and Statistical Analysis

Location: Remote – EST time zone

DiD/PSM modeling, causal inference, AB testing, demand forecasting, and statistical background. Tech stack is Python, PySpark, SQL, Databricks.

Responsibilities:

• Develop and implement causal inference models such as DiD and PSM to assess the impact of various interventions and programs.
• Design and conduct A/B tests to evaluate the effectiveness of marketing strategies, product changes, and other business initiatives.
• Perform rigorous statistical analysis to derive actionable insights from complex datasets.
• Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions.
• Ensure the accuracy and reliability of models through continuous evaluation and refinement.
• Communicate findings and recommendations to stakeholders through clear and concise reports and presentations.
• Stay up-to-date with the latest advancements in data science, causal inference, and statistical methodologies.

Qualifications:

• Proven experience in developing and applying causal inference models, including DiD and PSM.
• Strong background in designing and analyzing A/B tests.
• Proficiency in statistical analysis and data manipulation using tools such as Python, PySpark, SQL and Databricks
• Excellent problem-solving skills and the ability to work independently and collaboratively.
• Strong communication skills, both written and verbal, to effectively convey complex concepts to non-technical stakeholders.
• A degree in Statistics, Data Science, Economics, or a related field is preferred.
• Preferred Experience:
• Previous experience in the retail or pharmacy industry.
• Familiarity with large datasets and data warehousing solutions.
• Knowledge of machine learning techniques and their application in business contexts.