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CAPE Analytics provides instant property intelligence for buildings across the United States. CAPE Analytics enables insurers and other property stakeholders to access valuable property attributes at time of underwriting, with the accuracy and detail that traditionally required an on-site inspection, but with the speed and coverage of property record pre-fill. 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 OPPORTUNITYAs a Director of Data Science, you’ll manage Data Scientists and Computer Vision/Machine Learning Engineers, and collaborate with Data Engineers, Software Engineers, Product, and Sales teams to build robust, scalable machine learning models for identification and annotation of the built world. Additionally, you will direct enhancements 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.
As a senior member of the team, you will also participate in brainstorming and establishing Tech and Product roadmaps at Cape.
CAPE’s insurance solutions have been adopted by leading carriers across the U.S., Canada, and Australia...but we are just getting started. Over the past 6 years, we’ve constructed an analytics platform purpose-built for deep learning. 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 a larger share of the problem, leveraging a radically expanded array of input data sources and advanced machine learning technologies.
THE TECH STACKCAPE 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 AWS-deployed cloud infrastructure.
WITHIN 1 MONTH, YOU’LL
- Onboard with CAPE’s engineering team to learn about our tech-stack, our software development process, and machine learning process.
- Get to know the broader team you’ll work with through 1:1s and by sitting in on team and project meetings.
- Get to know your team, learning about their current projects, understanding their career goals, and setting up effective team coordination.
- Complete an onboarding project - aimed at improving our development process.
WITHIN 3 MONTHS, YOU’LL
- Understand CAPE’s near- and long-term product strategy and understand our roadmap.
- Partner with Recruitment to continue building out the team and set new hires up to contribute effectively against the engineering roadmap.
- Be an effective partner in technical planning, coordinating multiple priorities and matching data scientists to projects based on skill-sets.
- Start to assist in Sprint planning and Quarterly planning with the team.
- Contribute to the design and automation of our MLOps pipeline.
WITHIN 6 MONTHS AND BEYOND, YOU’LL
- Understand skill sets needed to meet our business objectives and build out the team accordingly.
- Deeply understand our model training, development and deployment process and drive improvements aimed at enhancing model quality while shortening project timelines.
- Be a mentor and guide to your team and be responsible for their career growth. Inspire a culture of innovation, risk-taking, and quality-first engineering at CAPE Analytics.
- Deeply understand the projects your team is working on and provide technical guidance leaning on your industry experience.
- Closely pair with senior technical staff to drive our Tech and R&D roadmap.
- Stay in touch with advances in the AI/ML world and bring relevant technologies in-house.
- Closely pair with Product Management to shape our future products and drive improvements in Product and Engineering interactions.
THE SKILL SET
- PhD in a STEM field with 10 years of hands-on industry experience or Masters in a STEM field with 12 years of hands-on industry experience required.
- 5+ yrs of experience of managing teams of ten or more senior data scientists required.
- Success in hiring, growing and retaining engineers of all experience levels.
- 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.
- Excellent communication skills: Able to effectively communicate in a clear, concise manner with experience communicating technical issues to cross-functional groups.
- Excellent technical planning skills with a proven track record of developing and executing complex technical projects.
THE TEAMYou 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.
*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.***