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Job Details

Data Scientist Finance

Location
New York City, NY

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Profile

The Role
Machine-driven investment strategies are only as good as their data. As traditional investment firms move towards quantamental investing and quantitative investment firms increasingly weave AI-driven strategies into their investment approach, the need for non-traditional financial data is growing at an astounding pace. The amount of money buy-side firms spend on alternative data is expected to grow from $400 million in 2017 to $1.7 billion in 2020. Within this space, email receipt data is becoming an increasingly essential part of firms’ data-driven workflow.
Armed with our rich, up-to-the-minute, behavioral dataset, you will play a key role in the global race for financial firms to adopt data-driven investment strategies. Your insights will have the power to inform essential strategic decisions at the largest hedge funds and banks in the world, and your research has the potential to shape the most nascent, but most exciting, field of quantitative finance. You will be an essential contributor to a Silicon Valley-based startup expanding its cutting edge, AI-driven email processing techniques to the world of financial technology in New York City.
You
You are deeply curious and thrive on seeing your ideas applied in the real world. You creatively and passionately work through problems with no clear-cut textbook solution, even if it means persevering through multiple iterations before you arrive at your solution. You are a strong advocate for well grounded science and data best practices within the company. You are expert at extracting signal out of flawed data to make valid inferences. And you are quick to learn, implementing new approaches when facing questions that demand it.
You are fascinated and energized by cutting-edge, data-driven techniques increasingly utilized in the world of quantitative finance. You are excited to leverage your data science skills to play a key role in the growing financial technology industry in New York City. You can imagine yourself thriving in the fast-paced, highly experimental environment of a data science startup.
Requirements
Skills
Ability to extract data from varied stores (t-sql, hadoop) and write code for models that can stand up to large datasets (python, spark)
Creative thinker excited by the prospect of ideating and evaluating experiments
Background in time series analysis and interest in time series-related machine learning research
PhD in Statistics, Math, Economics, Machine Learning, AI, Finance, Financial Engineering, related discipline or equivalent experience
Excited by a high learning curve in the field of financial technology
Excellent written and oral communicator of data-driven findings
Compensation and Benefits
Highly competitive salary and benefits
Stock grants pre-IPO at a company backed by top investors
Take unlimited, responsible vacation
Great office in Midtown
Opportunity for professional development (conferences, peer reviewed papers)
Trips to sunny California!
About Us
**MEMBERS ONLY**SIGN UP NOW***. provides intelligent email solutions for users and competitive intelligence for businesses. The largest, most valuable and as yet untapped data on earth is in mail; 3x larger than the worldwide web. Through our user base of more than 3 million users in the United States, we empower investors, brands, and technology companies to understand trends in the marketplace and gain deep insights into consumer behavior patterns.
As a team, we’re collaborative, engaged, and committed to continually improving as we serve our mission. None of us are on an island-- we trust our teammates to lend a hand when we’re stuck and our egos take a backseat to figuring out the best approach to tackling problems. We’re energized by tough problems and are excited to know that a challenge ahead of us does not have a textbook solution. Finally, we’re always in a posture of learning-- there is a lot we do not know but that does not hold us back from making an attempt at solutions. We lead thorough blameless postmortems to become better analysts, scientists, and leaders.

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