Senior Data Scientist I

Summit Health•Birmingham, AL

8d•$123,000 - $247,000•Remote

About The Position

The Senior Data Scientist I is responsible for performing a variety of statistical analysis and machine learning tasks, ranging from designing and evaluating experiments, conducting research, developing predictive and causal models, building marketplace optimization algorithms, to informing decision-making through insights, data storytelling, and reporting. This role collaborates closely with diverse teams (including product managers, engineers, and fellow data scientists) to advance our understanding of the customer, make predictions to improve our products and experiences, apply causal inference methods, and design and measure experiments.

Requirements

Master’s degree or equivalent in Data Science, or Mathematics
3 years of experience working with statistical analysis and machine learning
24 months of experience applying advanced causal inference methods, including treatment effect estimation, counterfactual analysis, and advanced causal machine learning approaches such as Double Machine Learning, Meta-Learners and Causal Forests, to evaluate the impact of product features, policies, or business strategies
24 months of experience applying statistical analysis and machine learning techniques to develop predictive models and solve complex business problems
24 months of experience working in Python and utilizing data analysis and machine learning, including Pandas, Numpy, LightGBM, Scikit-Learn, PyTorch, and causal inference libraries including EconML and Statsmodels
24 months of experience consulting with internal teams regarding model performance and translating analytical findings into actionable insights and decision strategies
24 months of experience utilizing data visualization tools, including Tableau, to communicate analytical results and business insights

Responsibilities

Utilize strong technical expertise and develop a thorough understanding of our data to drive insights in the areas of customer acquisition, engagement, retention, and loyalty through causal inference and experimentation methods.
Contribute to organizational maturity in data storytelling and best-in-class ML and statistical approaches.
Design product experiments to test business hypotheses, apply rigorous statistical analyses, and interpret the results to draw impactful insights.
Drive clarity and solve ambiguous business problems using data-driven approaches.
Contribute to the data science roadmap and priorities within area of ownership.
Utilize experience with exploratory data analysis, statistical analysis and testing, experiment design and model development, and knowledge of how to leverage these skills to solve complex business problems.
Apply advanced causal inference methods, including treatment effect estimation, counterfactual analysis, and advanced causal machine learning approaches such as Double Machine Learning, Meta-Learners and Causal Forests, to evaluate the impact of product features, policies, or business strategies.
Work in Python and utilize data analysis and machine learning, including Pandas, Numpy, LightGBM, Scikit-Learn, PyTorch, and causal inference libraries including EconML and Statsmodels.
Utilize data visualization tools, including Tableau, to communicate analytical results and business insights.
Consult with internal teams regarding model performance and translate analytical findings into actionable insights and decision strategies.
Apply statistical analysis and machine learning techniques to develop predictive models and solve complex business problems.