Manager of Data Science

| Greater NYC Area
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The Company


Orchard is transforming the way people buy and sell their homes. Simplifying it, to the way it should have always been; fair and true to market, straightforward, easy. Every year in the U.S., $1.5 trillion of single family residences transact, generating over $120 billion of fees in a process that has changed little in decades. For the average American, the home purchase and sale process takes months, creates anxiety and is filled with uncertainty and hassle.
Orchard offers a modern alternative, making one of life's biggest decisions -- the sale and purchase of a home –stress free, fair and simple.
Orchard launched in 2017 and was previously known as Perch. The company is headquartered in New York City, has 150+ employees and has grown 10x year over year. We have raised over $300 million in financing from top tier investors including: Firstmark, Accomplice, Navitas and Juxtapose.

 

We are proud to be recognized by Inc. Magazine as a best workplace of 2020 and have a 4.9 Glassdoor rating!


Role and Responsibilities:


Orchard's Data Science team is responsible for building the predictive models that help our underwriters make intelligent decisions when valuing homes. We use our skills in statistics, applied mathematics, computer science, and software engineering to value off-market real estate as accurately as possible. We also develop algorithms that power Orchard's consumer search capabilities, using computer vision to identify home features and enable a new kind of home discovery.
Our goal is to incorporate first-party and third-party data in order to continually refine and improve our home valuations over time, using techniques as simple as linear regression and as complex as neural networks, whatever delivers the best result. This role is very performance-oriented: the accuracy of our models very directly impacts the profitability and success of our business.
We are currently seeking a driven, results-oriented Data Science Manager for our team. The ideal candidate is pragmatic and has great instincts for which modeling changes are most likely to bear fruit. Most Importantly, the Data Science Manager must be able to drive the team to obtain the best possible performance and is unabashed in providing feedback and even engaging in conflict if need be in order to make the team successful. The existing team is small (two data scientists), so it is expected that the Manager would be a player-coach, spending about 75% of their time making substantive individual contributions. The candidate should be accustomed to delivering results in hours and days rather than weeks or months.

 

This position reports to the Head of Investments & Data Science.


Professional Qualifications:

  • 5+ years of data science experience required
  • Prior team management experience for a minimum of 2 years required
  • Entrepreneurial - comfortable talking to stakeholders to understand business needs, running small tests to validate assumptions, and refining requirements based on results
  • Self-motivated and gets stuff done
  • Understanding of probability theory and statistical inference - required
  • Proficiency in Python - required
  • Knowledge of SQL - required
  • Experience with neural nets/computer vision - preferred

Personal Qualifications:

  • Results orientation with a high motor and an incredible attention to detail; able to drive projects from planning to completion with limited oversight
  • Superior analytical skills and attention to detail with a data-driven mentality
  • A low ego and can-do attitude; willingness to admit mistakes and work to remedy them
  • Demonstrated communication and interpersonal skills to work with diverse stakeholders and  cross-functional teams
  • Flexibility to prioritize deliverables and re-prioritize them at a moment’s notice
  • Entrepreneurial mindset--you see ambiguity as opportunity and obstacles as chances to build new solutions  
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Location

Our NYC headquarters is located in the Financial District near the World Trade Center with plenty of restaurants and fast-casual spots in the area, and easily accessible by various transportation stops close by. Check out this video to learn even more: https://orch.co/nyc-office

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