Role overview
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.
What you'll work on
- 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, regularization, dimension reduction, and model explanation.
- Prior experience with Natural Language Processing (NLP) methods such as Word and Contextual Embeddings, Named Entity Recognition, Topic Modeling, large-scale Document Classification, and Sequence-to-Sequence Models is a significant plus.
- Ability to apply software engineering best practices to build scalable, reliable ML pipelines. Comfortable prototyping algorithms, iterating quickly, and working collaboratively.
- Prior experience working with large-scale datasets (Millions of records) using SQL and Spark.
- Excellent written and verbal communication skills, prior experience explaining assumptions, conclusions, and methodology to technical and non-technical audiences.
What we're looking for
- Competitive salary
- Company Equity
- Exceptional benefits package
- Flexible Vacation & Paid Time Off
- Employer-matched 401(k) plan
- A fun environment where work-life balance is valued