THE ROLE
As a Data Scientist on the Digital Experience Insights team, you'll lead high-impact analytics initiatives, turning complex data, platform, and time-series data into rigorous predictive models, causal inferences, and strategic product metrics. You'll also apply advanced statistical and quantitative research methods to some of our most complex datasets, but the core of the role is turning that analysis into decisions leadership can act on. This role sits within a fast-moving analytics organization where the specific problems shift over time, so we're looking for someone with strong research rigor who can adapt it to whatever question is in front of them.
WHAT YOU'LL DO
- Advanced Predictive & Statistical Modeling: Develop ML models spanning predictive modeling, time-series prediction, clustering, churn reduction, anomaly detection, and recommendation systems.
- Quantitative Research: Apply advanced computational methods and statistical techniques, including multilevel models and Statsmodels, to massive datasets.
- Executive Visualization: Create executive-level visual analytics that make complex findings accessible to senior stakeholders.
- Stakeholder Collaboration: Partner directly with Product, Engineering, and Business leaders to translate complex analytical findings into actionable strategy.
- We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.
WHAT YOU BRING
- Master's degree (MS) or Ph.D. in Computational Methods, Physical Science, Statistics, Applied Mathematics, Data Science, or a related quantitative discipline.
- 5 to 8+ years applying machine learning, statistical modeling, and quantitative research.
- Proven track record in quantitative research methodologies and experimental design, with publications or presentations of scientific or computational research, or patents.
- Advanced proficiency in Python and SQL.
- Hands-on experience with ML and statistics libraries such as Scikit-Learn, TensorFlow/Keras, PyTorch, Pandas, and Statsmodels.
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