R&D Data Scientist: Mathematical Modeling and Optimization
(Fully-remote US position) About LiftLab Liftlab is the leading provider of science-driven software to optimize marketing spend and predict revenue for optimal spend levels. We call this the Science of Marketing Effectiveness. Our platform combines economic modeling with specialized media experimentation so brands and agencies can clearly see the tradeoffs of growth and profitability. With decades of experience in marketing analytics and data science, our team of industry experts and thought leaders is proud to enable leading and emerging brands such as Cinemark, Express, Hanna Anderson, Lulu & Georgia, Pandora, Sephora, Skims, Tory Burch, Thrive, and Vionic, with our cutting-edge solutions and strategic guidance. Job responsibilities
- Develop new algorithm-based features of LiftLab’s marketing measurement and optimization platform
- Performs diagnostics and root-cause analysis and provide fixes
- Works with Data Science and Engineering to implement these features into LiftLabs product and workflow
Course work/experience:
- Data manipulation
- SQL
- Operating on big datasets in Python
- Data visualization
- Mathematical optimization
- Linear optimization concepts
- Nonlinear continuous optimization
- Linear algebra
- Mathematical modeling
- Using parametrized systems of equations to represent real-world systems
- Statistics
- Multivariate regression
- Clear understanding of Maximum Likelihood estimation and computational methods to find MLE parameters
- Bayesian concepts
- Hypotheses testing
Education requirements Graduate degree in Applied Mathematics, Scientific Computing, Operations Research or related field. We will consider holders of Bachelor degrees with relevant experience Skills/Aptitude
- Engineering and detective mindset
- Both to diagnose data and existing algorithms and to develop new analytics functionality
- Pragmatic approach to real-world problems
- Focus on problem solving over applying specific models
- Willingness to make approximations and assumptions rather than find “the” optimal solution
- Ability to combine multiple techniques and models to solve end-to end-problems
- Communication and collaboration skill
- Ability to convert non-technical requests into project specifications
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