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.
The Principal Data Scientist will play a key role in internal and external data science and AI products along with advisory services. The use cases include real time provisioning of insights leveraging unstructured data systems as well as big data systems with complex reporting and business intelligence requirements. This coworker will be responsible for researching, designing, developing and implementing machine learning algorithms and data analytics for internal CDW Marketing and Sales related data products. This coworker will leverage CDW’s AI Labs and big data environment to create insights and data products to help CDW learn more about its customers, as well as enable those coworkers interfacing with the customers (primarily sellers and marketers) to be more efficient and effective in doing their work.
Reporting to the Manager of Data Science, this coworker will ensure that machine learning algorithms and data analytics are designed, implemented appropriately, and the results are compiled into meaningful visualizations and shared with relevant stakeholders, while being responsible for enabling data science and technology best practices.
Key Areas of Responsibility
- Work directly with client stakeholders to develop technical solutions for business cases.
- Develop Data Science and Machine Learning coding and design standards.
- Translate business questions into specific hypothesis and quantitative questions that can be answered using data science and machine learning methodologies.
- Apply statistical or machine learning knowledge pertaining to the use cases to specific business problems and data.
- Apply machine learning techniques (both supervised and unsupervised), data mining techniques, performing statistical analysis and building high quality prediction systems.
- Keep abreast of the new technologies and trends in the AI and Data Sciences.
- Keep evaluating new tools and technologies in this space and form a point of view.
- Lead the consultative development of tailored solutions to business problems that leverage thought leadership.
- Share the evaluation criteria, fitment, pros and cons of various technologies with internal and external customers.
- Partner with business and enterprise data leaders to provide technical advice on data technologies and trends.
- 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 MapReduce, Spark, etc.).
- Guide and mentor data scientists
- Provide consulting to Technology and Data Engineering organizations on best practices for designing applications to enable easy insights; be an expert on large-scale data sciences.
- Actively participate in the industry externally through research, white papers, or presenting at conferences.
Education and/or Experience Qualifications
- Bachelor’s degree with 10+ years of experience in Statistics, Math, Computer Science or Engineering in related field
- Master’s degree or PhD with 6+ years of experience (Statistics, Computer Science, Physics, Engineering, etc.)
- Experience as a Data Scientist or Machine Learning expert.
- Experience with NLP, NLU and NLG capabilities.
- Experience in managing implementation projects that utilize big data, advanced analytics and machine learning technologies.
- Applied machine learning experience on large datasets/sparse data with structured and unstructured data.
- Experience working with Big Data technologies, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB) is a plus.
- Excellent understanding of machine learning algorithms, processes, tools and platforms and ML concepts like multilabel classification, personalization, recommender systems, etc.
- Practical experience with deep learning, neural networks, CNN, RNN, NLP, TensorFlow, keras, random forests, classifiers or Artificial Intelligence and their optimizations for efficient implementation.
- Strong understanding of data and information architecture, including experience with Big Data, Relational databases, streaming and batch data processing.
- Ability to effectively present information, interact with and respond to questions from managers, employees, customers and vendors.
- Demonstrated experience in teaching and/or mentoring professionals.
- Execute requests with strong attention to detail and strong time-management skills.
- Passion to evangelize data science, teach others and learn new techniques.
Data Science and Advanced Analytics Required Qualifications:
- Expert Level - Experience with scripting languages use (Python, R, Jupyter Notebooks, Java, Scala).
- Expert Level -Data Warehouse Solutions: Redshift, Snowflake, Postgres.
- Expert Level - Big Data technologies, Azure, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB).
- Expert Level -Workflow management: Airflow, Oozie, etc.
- Expert Level -Cloud storage: Azure or GCS.
- Intermediate Level -Data Visualization Solutions: PowerBI, Tableau etc.
- Expert Level -Data Science Workbenches: Cloudera, SAS etc.
- Expert Level -Data Exploration: Alteryx, TalenD, H2O etc.
- Advanced Level – GitHub, Maven etc. – Modern code organizer and build process for about half of our applications.
- Advanced Level – Expert at Jenkins – Modern build executor.
- Advanced Level – Containers – Modern build with microservices.
- Advanced Level – Swagger – Experience with modern features for the API including an automatically generated user interface.