Senior Data Scientist, Ring Data Science and Engineering

3 weeks ago

Irvine, California, United States Amazon Full time
About the Role

The Senior Data Scientist within Ring Data Science and Engineering plays a pivotal role in shaping how we carry the voice of our customers. We strive to understand their behaviors and preferences in order to provide them with the best experience connecting with the places, people and things that matter to them.

This role will build scalable solutions and models to support our business functions (Subscriptions, Product, Customer Service). By leveraging a range of methods including statistical analysis and machine learning, you will explain, quantify, predict and prescribe in support of informing critical business decisions.

You will translate business goals into agile, insightful analytics. You will seek to create value for both stakeholders and customers and inform findings in a clear, actionable way to managers and senior leaders.

Key Responsibilities

Drive shared understanding among business, engineering, and science teams of domain knowledge of processes, system structures, and business requirements.

Apply domain knowledge to identify product roadmap, growth, engagement, and retention opportunities; quantify impact; and inform prioritization.

Advocate technical solutions to business stakeholders, engineering teams, and executive level decision makers.

Lead development and validation of state-of-the-art technical designs (data pipelines, data models, causal inference, predictive models, data insights/visualizations, etc)

Contribute to the hiring and development of others

Communicate strategy, progress, and impact to senior leadership

A Day in the Life

Translate/Interpret

  • Complex and interrelated datasets describing customer behavior, messaging, content, product design and financial impact.
Measure/Quantify/Expand

  • Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.
  • Analyze historical data to identify trends and support decision making.
  • Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
  • Provide requirements to develop analytic capabilities, platforms, and pipelines.
  • Apply statistical or machine learning knowledge to specific business problems and data.
Explore/Enlighten

  • Formalize assumptions about how users are expected to behave, create statistical definition of the outlier, and develop methods to systematically identify these outliers. Work out why such examples are outliers and define if any actions needed.
  • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.
  • Make decisions and recommendations.
  • Build decision-making models and propose solution for the business problem you defined.
  • Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication.
  • Utilize code (Python/R/SQL) for data analyzing and modeling algorithms.

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