Data Scientist Palo Alto, CA

You are a data scientist, an experienced quantitative thinker who wants to develop further as both a data scientist and an engineer. You are skilled at finding the precise mathematical kernels of real-world business challenges faced by leading international financial institutions. You want to apply your expertise in statistics and computer science using Quantifind’s state-of-the-art infrastructure to solve problems in Natural Language Processing, such as Entity Resolution, Document Classification, building Knowledge Graphs, and Topic Modeling.

You are excited to work at a fast-growing company where you will have the chance to expand your scientific and engineering skills in new areas. You share Quantifind’s commitment to winning together and are eager to see your coworkers build on the technical foundations you will be creating. You are passionate about maintaining the high scientific and engineering standards required to enable your peers.

Above all, you are a curious and independent problem solver who is motivated to find a place where your skills can have a real impact.

You’re looking to explore a client-facing role at the intersection of software and consulting. You are obsessed with the idea of delighting clients at some of the world’s leading Financial Institutions, helping them identify financial crimes.

You have always been a strong critical thinker and know how to problem solve.

You are an excellent communicator with the ability to distill complex concepts into clear and effective verbal and written presentations. You are comfortable working closely with Sales, Product, and Technology teams to promote a Customer Centric culture.

You have an entrepreneurial spirit with a deep sense of personal accountability for work product quality and always take the initiative to propel work products to new heights. You maintain a positive attitude even in a deadline-driven environment, bring out the best in others, and enjoy contributing to the success of the team.

Quantifind is the Risk Intelligence company. We help some of the world’s biggest banks identify money laundering and fraud. Quantifind also works with government agencies, using the same platform, to uncover criminal networks and support national security. Unlike other players in this space, Quantifind delivers results through an AI-driven software-as-a-service (SaaS) solution with consumer-grade user experiences.

Quantifind’s Graphyte platform uncovers signals of risk across disparate and unstructured text sources using artificial intelligence and natural language processing techniques. In financial crimes risk management, Quantifind’s solution uniquely combines internal financial institution data with public domain data to assess risk in the context of Know Your Customer (KYC), Customer Due Diligence (CDD), Fraud Risk Management, and Anti-Money Laundering (AML) processes.

Today, these compliance processes are burdened by ever-increasing regulatory responsibilities and an expectation of frictionless transactions. Legacy technologies demand increasingly more human resources as the operations expand;
Quantifind’s solution offers a way to cut through the inefficiency and enhance effectiveness simultaneously. Demand for this solution is fueling our rapid growth.

To help you succeed, we provide a supportive environment that fosters collaboration between teams and team members, where learning and professional growth are considered a key part of your success, and of ours. We offer a flexible work environment with a family-friendly work-life balance.

• Master’s degree or higher in Statistics, Mathematics, Computer Science, or a related quantitative field.
• At least 2 years of professional industry experience, in addition to your academic experience.
• Proficient in two or more of the following:
  Scala / Java, Python, R.
• Outstanding quantitative and analytical ability. Able to take less-than-precise business requirements and translate them into Quantitative problems that you enjoy solving.
• In‑depth knowledge of Statistics / Probability / Machine Learning, including foundations such as bias/variance trade‑off,…