Walmart Global eCommerce is comprised of Walmart.com, VUDU, SamsClub.com, and our technical powerhouse @WalmartLabs. Here, innovators incubate next gen e-commerce solutions in real-time. We integrate online, physical, and mobile shopping experiences for billions of customers around the globe. How do we do it? We continuously build and invest in new technology including open source tools and big data innovations. Data scientists, front and back-end engineers, product managers, and web and UX/UI teams collaborate alongside e-commerce experts to envision, prototype, and bring revolutionary ideas to life in a dynamic, flexible and fun work culture.
We are looking for an Associate Director, Machine Learning Scientist who will help us strategically leverage the vast amounts of data from the World’s largest Omni-channel retailer to better serve the Customer. Your primary focus will be applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our products, using advance machine learning techniques.
- Leverage and organize data model, with marketing data, from various omni-channel sources, including internal platforms, media partners, agencies, store and online transaction data, and other third party sources.
- Process, cleanse, and verify the integrity of data used for analysis.
- Build and leverage tools that allow for democratization and easy consumption and analysis of data across the organization.
- Build AB and Multivariate testing framework to support a robust learning agenda and provide ongoing behavioral feedback to constantly improve performance.
- Implement machine learning and artificial intelligence models to improve conversion rates and campaign effectiveness, and work with engineering to implement algorithm optimization.
- Build funnels to better understand customer behavior and leverage them to improve conversion rates.
- Drive deep customer analysis with focus on predicting and improving customer LTV.
- Drive models that focus on incrementally and profit margin growth.
- Lead segmentation analysis and testing to better serve the customer in more relevant ways.
- Support both MMM and MTA measurement models (Traditional Marketing Mix as well as Multi Touch Attribution), and educate the organization when each should leverage, depending upon business goal and desired insight.
- Lead ad-hoc analysis and present results in a clear manner.
- Create automated anomaly detection systems and constant tracking of its performance.
- A Principal Data Scientist is responsible for collaborating with a business segment to determine long-term vision and provide analytical support and guidance. A Principal Data Scientist analyzes large data sets to develop multiple, complex custom models and algorithms to drive innovative business solutions. Principal Data Scientists work on large project teams in order to provide analytical support and guidance to a large project team (for example, email targeting, business optimization, consumer recommendations) within Walmart eCommerce. The algorithms built
- Build learning systems to analyze and filter continuous data flows and offline data analysis.
- Combine data features to determine search models.
- Conduct advanced statistical analysis to determine trends and significant data relationships.
- Develop machine learning models to apply test data algorithms to future data.
- Develop models of current state in order to determine improvements needed.
- Drives the execution of multiple business plans and projects
- Ensures business needs are being met
- Interpret data to identify trends to go across future data sets.
- Lead project teams to develop innovative solutions to drive innovative business solutions.
- Lead the implementation of data modeling solutions.
- Promotes and supports company policies, procedures, mission, values, and standards of ethics and integrity
- Provide guidance to cross-functional leadership regarding data modeling possibilities.
- Provides supervision and development opportunities for associates
- Research new techniques and best practices within the industry.
- Review business strategies to ensure alignment across initiatives.
- Scale new algorithms to large data sets.
- Train algorithms to apply models to new data sets.
- Translate business needs into data modeling initiatives.
- Utilize system tools including (MySQL, Hadoop, Weka, R, Matlab,ILog).
- Validate models and algorithmic techniques.