$80K — $100K *
As a Product Data Scientist you will have the opportunity to collaborate with product and engineering teams to work on Survey Design and Measurements, Identity, Trust & Safety and more. This includes product analyses, validation and establishing statistical methodology standards and development. The right candidate has the ability to independently research, develop and maintain products that align Lucid capabilities with market claims.
This role is open to candidates living in the United States to work remotely or from one of our US-based office locations (New Orleans, Dallas, New York)
The ideal candidate for this role will have 3+ years of hands-on professional data science experience
Supports research and discovery phase for new and existing products. The primary areas include but are not limited to trend analyses, outlier detection, generalization, harmonization, as well as working with different data sources.
Analyze large and diverse datasets to extract impactful insights that can drive product strategy. Collaborate with cross-functional teams to design, implement, and test new and existing measurement products- develop statistical modeling and methodologies.
End-to-end development and maintenance of Lucid’s measurement products.
Ongoing evaluation and validation of both internal and external products to ensure Lucid’s success.
Communicate insights and recommendations through visualizations and presentations that will resonate with a wide range of audiences.
Minimum 3+ years working experience in data science capacity.
Minimum Master's degree or equivalent in Statistics, Quantitative Sciences, Data Science, Operations Research or other quantitative fields
Ability to manipulate, analyze, and interpret large data sources
Deep understanding of advanced statistical techniques and concepts (e.g. properties of distributions, hypothesis testing, parametric/non-parametric tests, survey design, sampling theory, experimental design, regression/predictive modeling and more)
Strong Knowledge of a variety of machine learning techniques (clustering, regression, decision trees and etc) and their real-world advantages/drawbacks.
Must have working knowledge in the application of statistical and modeling techniques
Proficient in Python (as statistical and ML package tools). Proficient in SQL and working with large-scale databases
Comfortable in researching and learning new methods, tools, and techniques
Nice to have
Experience in media measurement and digital attribution
Experience in online survey methodologies
Experience employing causal inference methods
Experience in Identity graphs and fraud detection methodologies
Experience working with big data technologies (e. g. Spark)
Valid through: 11/9/2021