We are looking for a “full-stack” data scientist with experience in machine learning and algorithm development to join our team. This role reports to the Director of Data Science and will work closely with Marketing, Product Management and others to support growth initiatives. An ideal candidate will take full ownership of researching and developing machine learning models and deploying them in production while bringing stakeholders on board with evidence-based reasoning and compelling storytelling.
- Research, design, and implement data models and cutting-edge algorithms on high-dimensional, fast-moving, unstructured and structured data.
- Work closely with Product Management, Marketing, Customer Success and other stakeholders to understand customer behavior, product usage patterns and trends and to make data-driven decisions and forecasts.
- Lead large-scale eCommerce projects across design and measurement (A/B test), multi-arm bandits, causal inference, forecasting and prediction.
- Lead customer segmentation and related modeling: propensity models, lookalike models, uplift models.
- Quantitatively evaluate user journey to identify experience gaps to drive user growth in the onsite commerce and monetization space.
- Build models to understand experiment results and customer behavior.
- Collaborate with the data engineering team to create data pipelines.
- Constantly learn and have a clear pulse on innovation across the digital marketing, data science, customer data, and analytics communities.
- Master’s or Ph.D. in statistics, physics, math or another quantitative field.
- Minimum 5 years of industry experience applying advanced analytics/machine to solve practical problems. Minimum 2 years experience if possess a Ph.D.
- Excellent statistical knowledge.
- Ability to initiate and drive projects to completion with minimal guidance
- Fluency in SQL and Python.
- A team player who can collaborate with engineers, product managers, marketing and other cross-functional teams.
- Excellent verbal and written communication skills, with high attention to detail.
- Experience with causal inference techniques, experimental design and/or A/B testing preferred.
- Experience in one or more of the following: signal processing, multimodal sensing, sensor fusion, time series, time-frequency analysis, and dynamical systems preferred.