Our mission at Cornea is to be a public good corporation that brings modern data and machine learning techniques to wildland fire management and disaster response. Every year, the intensity and scope of wildland fires increases with climate change -- average acres burned per year nationally has increased from 2.1M in the 80s to 7.8M now. And yet, firefighters have very few tools to leverage technology, historical fire data, and the massive amount of data available about weather, ground conditions, and terrain. At Cornea, we are building a machine learning enabled suite of tools to inform critical decision-making for both emergency operations and planned events to increase safety and effectiveness in combating wildland fires.  

Cornea is a seed stage company in incubation at Hangar, a firm that funds and builds technology companies tackling public sector market challenges. With US annual spend at $5B for wildland fire suppression and a quickly increasing need for collaborative and predictive tools, we see a huge opportunity in this space and are excited to be a part of tackling this important challenge.

Cornea is seeking candidates for Lead/Principal Data Scientist to be located in New York, NY (or remotely within the United States). With fire and fire spread prediction sitting at the crux of Cornea’s mission, you will be leading the effort for our key core offering -- this role has the potential for huge impact on our trajectory, with expanded team management opportunities as we grow.  The data science work will be focused on improving fire prediction algorithms with specific focus on high quality evaluation methodology. You will also work closely with the product and engineering teams to improve data accessibility and efficiency of algorithm iteration. Additionally, you will be responsible for interfacing with the research and academic community in this space and will lead the effort to publish our findings in academic research journals. The ideal candidate is a strong data scientist, a critical thinker and a good communicator who can define and lead important initiatives. Get in on the ground floor of our expanding team! 

-We move quickly and deliver amazing tools and products for disaster response collaboration and fire analysis.

-We leverage existing research and methodologies to analyze and build models for fire suppression and disaster planning. We build platforms and resources to connect and share our findings and the massive amount of data available about weather, ground conditions, and terrain.

-We design and run statistically sound experiments to further uncover predictors of performance.

-We work closely across our organization to frame and tackle goals, both mathematically and within the business context.

-We collaborate and communicate with the fire community to share rationale and findings from models and analyses and facilitate firefighting effectiveness.

-M.S. or Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, or other quantitative field. (Ph.D. preferred)

-5+ years of industry experience delivering and scaling successful and innovative machine learning products both in scope and on time.

-Strong skills in statistical languages (e.g. Python/R) and querying languages (e.g. SQL).Experience in production-level coding with broad knowledge of healthy code practices.

-Comfortable in a startup, wearing multiple hats while staying focused on the most important deliverables.

-Excellent business acumen, with proven ability to understand, analyze and problem solve business goals in detail, developing effective solutions with clients.

-Experience working with the academic community and publishing papers in research journals is a plus. 

-With the above said, we always encourage people of all backgrounds and experiences to apply. We understand that job listings don't always allow your unique work history to shine so we invite you to show us what you know! 

Seniority Level: Senior level

Cornea Inc. does not discriminate on the basis of race, color, religion, religious affiliation, national origin, alienage or citizenship status, age, sex, sexual and other reproductive health decisions, creed, disability, sexual orientation, gender or gender identity or expression, marital or partnership status, pregnancy, arrest or conviction record, caregiver status, military or veteran status, actual or perceived status as a victim of domestic violence, sexual violence or stalking, unemployment status, credit history or any other basis prohibited by applicable law.