Mathematical Scientist

Pit.AI Technologies is taking on one of the most exciting challenges of our time, namely solving intelligence, in one of the most competitive industries ever, investment management!

The team enjoys the duality of solving humongous research and engineering problems, while remaining focused on very concrete shorter-term breakthroughs. We believe in excellence, humility, trust and inclusion, and every team member has a veto power on who we hire.

We adopt a finance-first approach to machine learning research, and we do not indulge in blindly applying solutions to machine learning problems in other domains to finance. Core to our research philosophy is the need to place as little assumptions on how the world behaves as possible, but rather let the data speak for itself, and use mathematical rigor to understand what the data is truly saying.

We are backed by Y Combinator, legendary Renaissance Technologies and First Round Capital co-founder Howard Morgan, and prominent tech VCs David Lee & Zal Bilimoria, to name but a few.

Pit.AI Technologies is hiring a Mathematical Scientist. You'll be at the core of our effort to solve intelligence for investment management, and as such, you'll work very closely with the CEO/CTO to improve our current generation of AI-Quants.

This role is an in-house mathematician role. As such, you'll be involved in formulating fundamental questions arising as part of the investment process as mathematical problems consistent with our research philosophy, and you'll be responsible for solving those mathematical problems. This role is central to our research efforts, and your output is expected to have a critical impact on our bottom line, both in the short term and in the long term.

The ideal candidate typically loves (and is capable of) testing and proving complex theorems and conjectures of her/his own, but prefers industry over academia as she/he is eager and impatient to see the impact of her/his work in real-life. She/he never shies away from finding closed-form solutions to complex mathematical problems, but she/he can also gauge the merits of numerical methods as an alternative when appropriate. As much as this is not an engineering role, the ideal candidate should be able to write prototype-quality Python code to test found mathematical results.

Pit.AI Technologies supports publications of methodological findings to top machine learning venues.

We strongly encourage applicants to read our blog posts () prior to applying, in order to get acquainted with our research philosophy and some of our works.

Requirements:

• Ph.D. / Post Doc in Maths from a top university or research group, with an emphasis on stochastic analysis, optimization, and/or functional analysis.
• Excellent graduate-level understanding of probability theory, stochastic analysis, functional analysis and optimization, as evidenced for instance by a French DEA in maths, or the Cambridge Part III, prior to your PhD in Maths.
• Strong intuition to guide the design of reasonable conjectures from the geometry of the problem and background knowledge prior to attempting any proof.
• No finance or machine learning experience is required.