Schwab is looking to leverage data to provide customers better products and grow business by designing and implementing scalable machine learning solutions for dynamic financial products, delivering business insights from unstructured data with clear narratives and creative visualizations, and partnering closely with the data infrastructure and platform teams to develop tools and automation. The role supports multiple business units across the Schwab enterprise with primary focus on Marketing Mix optimization modeling.
Requirements
- Knowledge of advanced techniques and concepts (regression, properties of distributions, time series analysis and modeling, statistical tests and proper usage, etc.)
- Knowledge of and experience with designing and implementing algorithms (Gradient Boosting Trees, GLM/Regression, Random Forest, Neural Networks, etc.), and the ability to articulate their real-world advantages and drawbacks
- Knowledge and experience in advanced data mining & modeling techniques (GLM/Regression, K-Means Clustering, Decision Trees, Random Forest, GNBC, NLP, AI, Social Network Analysis, etc.), and the ability to articulate their suitability for a given challenge
- 2+ years’ experience in delivering data science/analytics solutions
- 2+ years’ experience in Python, data engineering, analytics role
- Software engineering and code versioning skills
- Data visualization experience
Responsibilities
- Get hands-on with big data as you hack through complex and messy data, and analyze it leveraging the latest algorithms and state-of-the-art techniques and tools
- Develop marketing mix model to measure channel efficiency and optimize marketing budget allocation
- Use open-source tools and platforms like Anaconda Enterprise, Python, R and SAS to apply algorithms and analytic models to big data problems
- Develop automated and algorithmic approaches to analyzing data and powering data products and predictions
- Work closely with business stakeholders to understand their needs, articulate how data science can help achieve objectives, and deliver regular updates
- Collaborate with Data Science Engineers to operationalize models
Other
- In-office collaboration
- Business acumen: Understanding the bigger picture for customers and the business and the know how to probe beyond stakeholders’ stated requests to understand what is truly needed to capture and drive business value
- Proactive, critical thinker, bias to action and a go-getter attitude
- Enthusiastic quick learner who loves to code
- Experience in financial services industry
