Senior Data Scientist, Ring Data Science and Engineering
Amazon
Full-time
On-site
New York, New York, United States
$143,300 - $247,600 USD yearly
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?
ABOUT RING
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.
(https://www.ring.com)
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 job 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.
- Bachelor's degree
- 4+ years of data scientist experience
- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- Experience with statistical models e.g. multinomial logistic regression
- 5+ years of hands-on experience in modeling and analysis, and in deploying machine learning / deep learning models in production.
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team
- Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Domain knowledge of comparable products and services
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
ABOUT RING
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.
(https://www.ring.com)
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 job 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.
- Bachelor's degree
- 4+ years of data scientist experience
- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- Experience with statistical models e.g. multinomial logistic regression
- 5+ years of hands-on experience in modeling and analysis, and in deploying machine learning / deep learning models in production.
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team
- Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Domain knowledge of comparable products and services
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.