The Data Science and Analytics team 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 Data Scientist is a key player on the Data Science & Analytics team. 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 “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 implemented appropriately, and the results are compiled into meaningful dashboard and reports and is shared with relevant stakeholders, while being responsible for enabling data science and technology best practices.Key Areas of Responsibility
- Translate business questions and concerns 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.
- Approach the problems with a business-oriented mindset to develop an understanding and documentation of business problems, product requirements and success metrics.
- Be the leading resource for sales and marketing.
- 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 MapReduce, 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.
Communication & Strategic Management
- Partner with engineering and product management teams to execute the data science roadmap.
- Serve as a central knowledge center for relevant data science tools and techniques.
- Keep current with technical and industry developments.
- Present insights and recommendations to audiences at the desired levels of understanding.
Education and/or Experience Qualifications
- MS in Statistics, Machine Learning, or Computer Science (or technical degree with commensurate industry experience)
- 2+ years of relevant work experience as a data scientist / Machine Learning expert
Other Required Qualifications
- Excellent understanding of machine learning algorithms, processes, tools and platforms and ML concepts like multilabel classification, personalization, recommender systems, etc.
- 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), and RDBMS Oracle is a plus.
- Experience working with web-analytics tools (ex: Google Analytics, Omniture).
- Practical experience with deep learning, neural networks, CNN, RNN, NLP, TensorFlow, keras, random forests, classifiers or Artificial Intelligence and their optimizations for efficient implementation.
- Experience using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
- Experience in predictive modeling.
- Passion to evangelize data science, teach others and learn new techniques.
- Expertise in SQL scripting language.
- Proficiency working with relational databases (Oracle, MySQL, PostgreSQL)
- 2+ years of frequent scripting languages use (Python, R, Jupyter Notebooks, Java, Scala).
- Ability to execute analytical experiments methodically while outputting reproducible research.
- Highly detail oriented with the ability to handle multiple projects simultaneously.
- Great communication skills and presentation skills.
- Experience working with cloud-based data warehouses such as Redshift, Snowflake, BigQuery.
- Experience with cloud-based personalization and machine-learning applications.
- Experience working for consumer or business-facing digital brands.