The Data Science and Analytics COE is responsible for leading the creation and development of the overall strategy and direction of data science and advanced analytics at CDW – including ensuring continuity and seamless extension of existing programs, the development of a short- and long-term vision and roadmap, and defining and institutionalizing the role that data and analytics play throughout the organization as the fuel that drives and shapes CDW’s priorities and serves as an accelerant for CDW’s progress.
This role was created to leverage the talents of expected college graduates especially those with an interest in the data and insights space. Responsibilities include: researching, designing, developing and implementing machine learning algorithms, data analytics and data visualizations for internal CDW Marketing and Sales related data products. CDW’s “big data” environment will be leveraged to create insights and data products to help CDW learn more about its customers, as well as enable those coworkers interfacing with the customers to be more efficient and effective in doing their work. You will work with CDWs experienced data scientists, engineers and analysts, will be mentored and will share ideas and technologies. The intent is also to ensure you will have a valuable experience and consider CDW for full-time employment post-graduation.
What you'll do:
• Improve existing methodologies by developing new data sources, test model enhancements, and fine-tune model parameters.
• Transform, standardize and integrate data sets to develop data marts for data science use cases.
• Develop data-processing and technical workflows for delivery of data-driven customer experiences.
• Retrieve, synthesize and present critical data in a format that is immediately useful to answer specific questions or improve system performance.
• Work as a data strategist and predictive modeler by researching, identifying and integrating datasets and innovative algorithms that drives products and services forward.
• Analyze historical data to identify trends and support decision making.
• Perform data exploration and foundational data analysis in support of long-term analytics objectives.
• Provide hypothesis and requirements to develop analytic capabilities, platforms and pipelines.
• Formalize assumptions about how systems are expected to work, create statistical definitions of outliers, and develop methods to systematically identify these outliers. Work out why such examples are outliers and define if any actions are needed.
• Develop predictive models to advance machine learning skills.
• Profile and optimize machine learning algorithms to meet performance requirements.
• Implement highly optimized data analytics processing algorithms on big data batch and stream processing frameworks (i.e. Hadoop, Spark, etc.).
• Apply machine learning techniques (both supervised and unsupervised), data mining techniques, performing statistical analysis and building high quality prediction systems.
• Create classification systems for key buyer and seller attribute data to support predictive analytics.
• Build and test yield optimization models to improve the sponsored listings marketplace.
• Implement recommendation and content relevance engines to increase buyer engagement.
• Currently enrolled in a full-time undergraduate, graduate or PhD college / university program in the areas of Mathematics, Statistics, Physics, Computer science / Engineering, Data Science, Analytics / Engineering, Information Sciences, Business Intelligence / Analytics.
Other Required Qualifications
• Excellent written and verbal communication skills with the ability effectively interact with all stakeholders
• Must be able to work independently and in a team-oriented environment
• Strong presentation skills, including ability to develop and deliver PowerPoint presentations
• Microsoft Office skills including, but not limited to Outlook, Excel, and Word
• Effective ability to multi-task in order to handle multiple duties at the same time throughout the day
• Applied machine learning experience on large datasets/sparse data with structured and unstructured data.
• Python, Java or other programming language
• Basic SQL Programming
• Exposure to Big Data technologies, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB), and RDBMS Oracle is a plus.
• Exposure to working with web-analytics tools
• Exposure to various algorithmic techniques: deep learning, neural networks, CNN, RNN, NLP, TensorFlow, Keras, random forests, classifiers or Artificial Intelligence and their optimizations for efficient implementation.