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 Architect 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 role will help evaluate and improve existing data structures and play a crucial role in creating evolutionary models for current and future products.
In this role the ideal candidate will work with various business and IT stakeholders to lead and contribute to the definition of an overarching data strategy and architecture that will align investments with the highest impact areas, while producing a suite of complementary models and technologies. This architecture will form the central platform which the data analysts, data engineers and data scientists will work in their day to day activities to generate value from all the data they analyze.
Key Areas of Responsibility
- Lead complex Big Data and Data Science Architecture and whiteboarding sessions.
- Develop CDW Data Science and Machine Learning platform and architecture standards.
- Work directly with client stakeholders to develop technical solutions for business cases.
- Work closely with internal technical and business stakeholders to shape the success of projects.
- Manage end-to-end Platform requests with minimal direction from Functional Managers and data scientists.
- Communicate server/ system issues, resolution plans, and recommend preventive actions to leadership/ business team.
- Work with third party vendors and consultants on system upgrades, updates/ changes through the problem resolution.
- Participate in capacity planning to ensure production environments are adequately sized and configured to meet current and projected demand.
- Participate in performance testing to ensure Big Data Architecture meets the needs of the data science and AI teams.
- 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.
- Guide and mentor data engineering and data science teams to raise collective technical expertise.
- Demonstrate a bias for architecture principles balanced with tactical timelines, cost and risks.
- Embrace and affect domain-centric design and architecture principles of security, scale, uptime and reliability.
- Define Micro services Infrastructure by analyzing currently used platform architecture, technology, and tools.
- Provide consulting to IT and data engineering organizations on best practices for designing applications to enable easy analytics; be an expert on large-scale data sciences.
- Actively participate in the industry externally through internet research, white papers, or conferences.
Education and/or Experience Qualifications
Bachelor’s degree in Computer Science, Information Systems or equivalent IT knowledge/experience.
10+ years of relevant work experience as a data scientist / Machine Learning expert.
Other Required Qualifications
Experience in leading architecture teams and managing implementation projects that utilize big data, advanced analytics and machine learning technologies.
Hands-on data architect with in-depth, hands-on knowledge of foundational data architectures such as data warehouses ETLs and in-memory OLAP models, as well as experience in NoSQL and cloud implementations.
Strong understanding of data and information architecture, including experience with Big Data, Relational databases, streaming and batch data processing.
Strong experience building end-to-end data view with focus on integration.
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, Azkaban
Expert Level -Cloud storage: S3, GCS
Expert Level -Data Visualization Solutions: Looker, Tableau etc.
Expert Level -Distributed logging systems: Pulsar, Kinesis 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
Experience working for consumer or business-facing digital brands.
Bachelor’s degree in Business, Math, Engineering, Statistic, Economics, Operation Research, Data Science, Computer Science or related quantitate field.