Ring is looking for an insightful and analytical Data Scientist with strong business and technical skills to join our rapidly growing Business Intelligence team. In this role, you will be partners to various executive, product management, engineering, quality assurance, finance, logistics, shipping, sales and marketing teams that power Ring. Your work will be instrumental to achieving its mission, be highly visible to Ring / Amazon leadership, and will drive key strategic company goals. 

You will develop advanced models and tools, conduct statistical analyses, evaluate large data sets, and create tailored predictive models and recommendation engines. Additionally, you will be instrumental in the championing data science within the organization. You will be structuring ambiguous problems and designing analytics across various disciplines, resulting in actionable recommendations ranging from strategic planning, product strategy/launches, and engineering improvements to marketing campaign optimization, customer servicing trending, and competitive research.

Responsibilities

• Selecting features, building and optimizing classifiers using machine learning techniques
• Evaluate clustering, decision tree learning, artificial neural networks, and educate the team on the real-world advantages/drawbacks
• Processing, cleansing, and verifying the integrity of data used for analysis
• Creating automated anomaly detection systems, scoring systems and constant tracking of its performance
• Anticipate, identify, structure, and solve critical problems using predictive modeling
• Partner with subject matter experts to document and understand the features and attributes to use them efficiently in the data models
• Manage multiple projects and proactively communicate issues, priorities, and objectives
• Clearly communicate the model outputs to the executive staff and how we can use the model to make business decisions
• Partner with subject matter experts to document and translate business requirements into technical requirements

Basic Qualifications

• Bachelor’s Degree in a quantitative discipline (e.g., Statistics, Math, Computer Science, Data Science, Analytics, or similar) 
• 3+ years working experience with data mining and advanced analytics
• 3+ years of experience in using predictive modeling to solve business problems
• 3+ years of experience with a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and understanding of the real-world advantages/drawbacks
• 3+ years of experience with big data tools or analytical software (e.g., Athena/Presto, R or SAS)

Preferred Qualifications

• Master’s Degree in a quantitative discipline (e.g., Statistics, Math, Computer Science, Data Science, Analytics, or similar)
• 3+ years of Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets
• 2+ years of experience with querying structured and unstructured data using tools like Athena, Spectrum and Redshift
• 3+ years of working with Tableau and Looker
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications

About Ring

Ring's mission is to reduce crime in neighborhoods by creating a Ring of Security around homes and communities with its suite of home security products and services. The Ring product line, along with the Ring Neighbors app, enable Ring to offer affordable, complete, proactive home and neighborhood security in a way no other company has before. In fact, two Newark, NJ neighborhoods saw an over 50 percent decrease in home break-ins after Ring Video Doorbells and Spotlight Cams were installed on 11% of homes in the communities from April-July 2018 when compared to the same time period in 2017. Ring is an Amazon company. For more information, visit www.ring.com. With Ring, you’re always home.

Ring LLC is proud to be an equal opportunity employer and provides equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity or genetics.