A day in the life…

Partner with key stakeholders to define success and develop measurement frameworks.

• Reconcile divergent demands from multiple stakeholders in a fast-paced work environment.
• Present the results to the stakeholders up to executive level and guide them to make the best use of analytics in their domain.
• Use advanced analytical and statistical practices to create thorough and actionable insights.
• Perform large-scale statistical analysis and develop and apply segmentation, predictive models, and forecasting models to key business problems.
• Deliver high quality solutions and recommendations to a variety of problems both independently and through the collaboration with team members and business partners to drive outcomes.
• Bring data to life through storytelling in a clear and meaningful way to audiences with mixed levels of technical expertise.
• Mentor more junior team members on analytics, storytelling, and communication best practices.
  
  Minimum Qualifications Include…
  
  
• 1-3 years hands-on professional experience in Data Science and Analytics.
• 1-3 years of strong coding skills in at least one statistical or programming language (e.g. R, Python) to import, process, summarize, and analyze data.
• Experience with utilizing data visualization tools to tell compelling data stories (e.g. Tableau, Shiny)
• Bachelor’s degree in mathematics, statistics, computer science, economics, operations research or in a quantitative field (or equivalent experience).
• A solid grasp of the scientific method and its application to Data Science.
• Fluency with descriptive and inferential statistical techniques, including experimental design (DOE) concepts and their application.
• Proficiency with statistical and machine learning algorithms (e.g. regression, decision trees, collaborative filtering, clustering, survival analysis, graph theory, etc.).
• Proficient in extracting large data sets from various relational databases using SQL (Amazon Redshift, Oracle, Teradata preferred).
  
  Our ideal candidate has…
  
  
  
• MS or PhD in mathematics, statistics, computer science, economics, operations research or in a quantitative field (or equivalent experience).
• Strong background and proficiency with advanced methods such as (neural networks, Bayesian networks, deep learning, recommenders, etc.)
• Experience of building and maintaining operational models processing large quantity of data in real-world production environments such as SageMaker, AzureML or Kubernetes.
• Passion and aptitude for turning sophisticated problems into concrete hypotheses that can be answered through meticulous data analysis and A/B testing.
• Deep knowledge of statistical and machine learning algorithms and experience developing and deploying them to production-level systems (using Scikit-Learn, mlpy, MICE, Caret, PyTorch, Keras, or TensorFlow).
• Experience working in a highly collaborative environment (e.g. code sharing, using revision control, contributing to team discussions/workshops, and document sharing).
• Experience developing and deploying data pipelines using Cloud Services (e.g. AWS).
• Experience using engines such as Apache Spark or Hadoop to analyze large datasets.
• Experience with data ETL, RESTful APIs.
• Strong coding skills in Python, R, Go, Scala, Java, C, and/or C++.
• Familiarity with e-commerce analytics topics (e.g., digital tactics, targeting and customer segmentation, lifetime value forecasting, and incremental response modeling, to name a few).