CAPE Analytics provides inspection-quality condition and property characteristics data for 110 million unique structures across the United States, derived from aerial imagery via advanced computer vision. This enables HELOC originators and investors, single-family rental investors, and loan traders to access valuable property attributes with the accuracy and detail that traditionally required an on-site inspection. Leveraging CAPE Analytics, these verticals have access to more accurate property valuations, improved decisions around bids, buys, and capital expenditure needs for portfolios, and increased efficiency by avoiding time-consuming research. Founded in 2014, CAPE Analytics is backed by leading venture firms and innovative insurers and is comprised of computer vision, data science, and risk analysis experts.

THE OPPORTUNITY

As a Senior Data Scientist on CAPE’s Data Science team, you’ll collaborate with Data Scientists, Computer Vision/Machine Learning Engineers, Data Engineers, and members across Software Engineering, Product, and Sales teams to build robust, scalable machine learning models for identification and annotation of the built world. Additionally, you will develop expertise in ground truth generation, model performance analysis, iterative model development, and unsupervised mapping of the feature space to bring scientific rigor, scalability, and robust performance to our core product offerings.

Over the past 6 years, we’ve constructed an analytics platform purpose-built for deep learning that has led us to be adopted by leading insurance carriers across the U.S., Canada, and Australia...but we are just getting started. On the heels of our recent $44 million Series C financing, we’re growing rapidly. In CAPE’s next phase, we’re setting out to solve the biggest problems in the Real Estate industry.

THE TECH STACK

CAPE leverages all available tools and technologies to build our best-in-class tech-stack, which affords us flexibility of fast-deployments, along with the stability to support aggressive SLAs for critical-path client APIs and applications. We build our models using Pytorch and Tensorflow, and leverage Python, Spark and Postgres across our GCP-deployed cloud infrastructure.

WITHIN 3 MONTHS, YOU’LL:

Develop scientifically rigorous, creative methodologies to continuously improve our machine learning models
Incorporate machine learning and data-driven decisioning into the core of our infrastructure
Explore and mine new data sources that will help optimize and validate our models
Link model capabilities to market needs by customizing models, designing and running validation studies

WITHIN 6 MONTHS, YOU’LL:

Contribute to design and automation of model training, model post-processing and evaluation pipelines at scale
Leverage the extensive data generated by CAPE in addition to data from external sources to generate structured knowledge about our feature space
Implement automated solutions for ensuring data quality and delivery
Contribute to peer mentorship, knowledge bases, and skills transfer

WITHIN 12 MONTHS, YOU’LL:

Present your results internally and externally
Defend your methodology and incorporate feedback from internal teams as well as customers
Improve model performance by identifying failure modes using supervised and unsupervised learning techniques
Ideate and implement data-driven methodologies to help scale model performance across geographical, climatic, and temporal dimensions

THE SKILL SET

PhD in a STEM field with 3 years of hands-on industry experience or Masters in a STEM field with 5 years of hands-on industry experience
A background in the Finance or Real Estate sector is strongly preferred. This includes familiarity with Real Estate data such as MLS and other public record data, Mortgage Loans, Automated Valuation Models, Asset Valuations, Cash Flow Analysis, Risk Analysis etc.
Excellent written and verbal communication skills, with the ability to understand and articulate business requirements and objectives to both technical and non-technical stakeholders.
Solid knowledge of statistical techniques, including hypothesis testing, statistical sampling, significance testing, statistical inference, maximum likelihood estimation, and experimental design, among others
Mastery of, supervised and unsupervised algorithms and their implementations, machine learning concepts including regularization, learning curves, optimizing hyperparameters, cross-validation, among others
Advanced knowledge and significant programming experience in Python programming or other scripting language including relevant libraries like numpy, pandas, SciPy, matplotlib
Familiarity with tools in the modern ML stack such as Spark, Jupyter, Docker, Git and cloud computing on AWS or GCP
Demonstrated expertise in building data tools for ETL, extracting data from SQL and NoSQL databases, and data analysis
Experience in building meaningful data visualizations using at least one scripting-based visualization tool such as matplotlib, d3.js or bokeh
Nice to haves: Experience with GIS systems. Experience with deep learning for computer vision.

THE TEAM

You will work with some of the smartest data scientists in the industry. They are passionate about the work they do and have collectively built the industry’s leading AI/Analytics product. Success only comes with great team culture, camaraderie, open communication and hard work. These are the qualities that you will experience and enjoy at CAPE.

We believe:

*Talent is critical, but best when tempered with humility

*Self-motivation leads to the best outcomes

*Open, direct communication is a sign of respect

*Teamwork drives success

*Having fun together is an important part of the job

***CAPE Analytics is an E-verify participant.***

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