Senior Data Scientist, Ring Data Science and Engineering

Senior Data Scientist, Ring Data Science and Engineering

Job ID: 2798199 | AMZN Dev Cntr Poland sp. z.o.o

Come build the future of smart security with us. Are you interested in helping shape the future of devices and services designed to keep people close to what’s important?

The Senior Data Scientist within Ring Data Science and Engineering plays a pivotal role in better understanding how customers interact with our products and how we can improve their experience. This role will build scalable solutions and models to support our business functions (Subscriptions, Product, Customer Service). By leveraging a range of methods with an emphasis on causal techniques, you will explain, quantify, predict and prescribe in support of informing critical business decisions. You will help the organization better understand customers and how to best impact them. 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 job responsibilities

1. Lead development and validation of state-of-the-art technical designs (causal inference, predictive tabular models, data insights/visualizations from EDA, etc).
2. Drive shared understanding among business, engineering, and science teams of domain knowledge of processes, system structures, and business requirements.
3. Apply domain knowledge to identify product roadmap, growth, engagement, and retention opportunities; quantify impact; and inform prioritization.
4. Advocate technical solutions to business stakeholders, engineering teams, and executive level decision makers.
5. Contribute to the hiring and development of others.
6. Communicate strategy, progress, and impact to senior leadership.

A day in the life

1. Translate/Interpret complex and interrelated datasets describing customer behavior, messaging, content, product design and financial impact.
2. Measure/Quantify/Expand by applying statistical or machine learning knowledge to specific business problems and data.
3. Analyze historical data to identify trends and support decision making.
4. Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
5. Provide requirements to develop analytic capabilities, platforms, and pipelines.
6. Explore/Enlighten by making decisions and recommendations.
7. Build decision-making models and propose solutions for the business problem you defined. Help productionalize them so they can be used systemically.
8. Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication.
9. Utilize code (Python/R/SQL) for data analyzing and modeling algorithms.

About the team

We started in a garage in 2012 when our founder asked a simple question: what if you could answer the front door from your phone? What if you could be there without needing to actually, you know, be there? After many late nights and endless tinkering, our first Video Doorbell was born. That invention has grown into over a decade of groundbreaking products and next-level features. And at the core of all that, everything we’ve done and everything we’ve yet to build, is that same inventor's spirit and drive to bridge the distance between people and what they care about. Whatever it is, at Ring we’re committed to helping you be there for it.

BASIC QUALIFICATIONS

1. Bachelor's degree.
2. Industry experience as a Data Scientist.
3. Experience of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience.
4. Experience with a wide variety of modelling approaches with an emphasis on causality (e.g. DML).
5. Hands-on experience in modelling and analysis, and in deploying machine learning / deep learning models in production.

PREFERRED QUALIFICATIONS

1. Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.
2. Experience managing data pipelines and helping develop ML Ops stacks.
3. Experience as a leader and mentor on a data science team.
4. Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue).
5. Domain knowledge of comparable products and services.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( to know more about how we collect, use and transfer the personal data of our candidates.