Our client who is creating an entirely new category of software-as-a-service for consumer brands -- the Customer Growth Platform. This first-of-its-kind platform enables automated digital engagement and personalized loyalty programs to transform customer experiences while delivering predictable and repeatable lifetime value growth for brands.
As a Senior Data Scientist on a Customer Growth Platform, you’ll develop predictive data products, such as product demand forecasts, customer lifetime valuation, personalized product recommendations, and more. Then you’ll develop optimization algorithms based on these predictive data products, for brands to use in their marketing and offers so they can efficiently maximize growth. The experimentation solutions you develop should prove the value of our suggestions, as well as help train future predictive models about what products and offers will drive customer engagement.
Day to day includes:
- Data platform design: You'll partner with software engineering and product management counterparts to design our data and machine learning infrastructure, including collection, storage, processing, experiment design, modeling, and finally automated optimization.
- Data normalization and integrity: we’ll be importing highly heterogeneous data from a variety of customers across thousands of locations with millions of customers. This will require clever solutions to ensure the data they send us is readily available, easy to use and reliable.
- Predictive modeling and data products: you’ll be the primary contributor to the models that power our forecasting and recommendations, which are at the core of this role and our Customer Growth Platform offering.
- Experimentation & impact analysis: outbound marketing automation, including paid media, product discounts and loyalty reward offers, often costs consumer brands real money. Through rigorous experiment design, including power analysis, and execution you will prove to our customers that our predictions are accurate and our optimizations deliver impact. When experimentation isn’t an option, you’ll develop causal inference models to measure impact.
- PhD in a quantitative discipline such as Statistics, Mathematics, Data Science, Business Analytics, Economics, Finance, Engineering, or Computer Science
- 3+ years of demonstrated success using machine learning model development to solve business needs
- Expertise in at least one statistical software package or languages such as R, Stata, Matlab, or Python
- Expertise using big data infrastructure and tools, such as Spark, SageMaker, Redshift
What’s on offer:
- Competitive Salary
- Equity/Stock Options
- Health, vision, dental insurance coverage
- Life Insurance, Short-Term Disability, Long-Term Disability
- Pre-tax Commuter Benefits and Ford GoBike Membership
- Pet Insurance and Dog-Friendly Office
- ARC Fertility Program
- Catered lunch and unlimited snacks
- Regular company outings and events