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 Director, Data Science and Analytics will play a key leadership role in internal and external data science and AI products along with advisory services. This leader will be responsible for a team of data scientists, data engineers and analysts who are involved in researching, designing, developing and implementing machine learning algorithms and data analytics for CDW Marketing and Sales related data products. This leader will leverage CDW’s data labs environment to create insights and data products to help CDW learn more about its customers, as well as enable our sales and marketing teams who are interfacing with the customers to be more efficient and effective.
Reporting to the Head of Data Science and Analytics at CDW, the ideal candidate will work with various business and IT stakeholders to lead and contribute to the definition of an overarching Data Science and AI strategy and Architecture that will align investments enabling growth, while producing insights through machine learning, deep learning, advanced analytics and technologies.
Key Areas of Responsibility
- Develop and Deliver Data Science and AI Strategy to address CDW's multi-year, multi-technology and multi-consumption model strategy.
- Lead the consultative development of tailored solutions to business problems that demonstrate thought leadership.
- Partner with business and enterprise data leaders to provide technical advice on data technologies and trends.
- Be a consultant for the business leadership and understand their challenges that could leverage insights for solving them, tailor POCs and proposals.
- Unlock insights and guide the business – everything from tactical site optimizations to broad level strategic direction that is grounded in data evidence and heavy analytical rigor.
- Lead and mentor a team of data scientists, machine learning engineers and data product managers to explore & process data from disparate sources, generate actionable insights, drive the use of advanced analytics methods to test hypotheses and perform analytical deep-dives to identify problems, opportunities and actionable insights
- Define and lead design and development of foundational growth enablers, including: self-serve analytics, customer insights, mission learning models for product recommendations and segmentation, market attribution, forecast and risk, etc.
- Discover areas of the customer experience and business that can be automated through machine learning.
- Build a team of business champions in enabling a data-oriented decision-making culture and iteratively work with them in adopting the insights and analytics.
- 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.
- Share the evaluation criteria, fitment, pros and cons of various technologies with internal and external customers.
- Lead complex Big Data and Data Science Architecture and whiteboarding sessions.
- Develop CDW Data Science and Machine Learning design, build, test and deploy standards.
- Apply statistical or machine learning knowledge pertaining to the use cases to specific business problems and data.
- Approach the problems with a business-oriented mindset to develop an understanding and documentation of business problems, product requirements and success metrics.
- 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.
- Provide consulting to the relevant data teams on best practices for designing applications to enable easy analytics; be an expert on large-scale data sciences.
- Communicate complex ideas clearly to tell a data story with the appropriate business language.
- Think “beyond the numbers” to deliver business relevant recommendations with audience in mind.
- Build, motivate & mentor teams of managers and specialists to grow their skills and careers.
- 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 field.
- 15+ years of relevant work experience in the data domain at different levels, technologies and lines of business.
Other Required Qualifications
- Experience in leading data science, analytics, AI, engineering and architecture teams in managing projects/products that utilize big data, advanced analytics and machine learning technologies.
- 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.
- Strong communication skills, both verbal and written with an ability to present and influence business decisions at all levels of the organization, including vendors and partners.
- Execute requests with strong attention to detail and strong time-management skills.
- Passion to evangelize data science, teach others and learn new techniques.
- Strong analytical and technical capability along with experience in business strategy to discover key insights in company’s product, customer, web and transactional data.
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 -UI/UX/Data Visualization Solutions: Looker, Tableau, node.js 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 – Experience with API based architecture
- Experience working with consumer or business-facing digital brands.
- Master’s degree in Business, Math, Engineering, Statistic, Economics, Operation Research, Data Science, Computer Science or related quantitative field.