Data Science/Quant Engineering Intern | Prattle Technology
We are the Prattle Analytics Technology team at Liquidnet. We develop proprietary NLP and ML systems to produce analytics that predict the market from corporate communications, news, and alternative data to outperform humans and machines alike. Our systems and algorithms automate traditional financial research, and bring technical accuracy, speed, and efficiency to all corners of the finance industry.
Portfolio managers, research analysts and other financial movers and shakers use our data to understand and anticipate relevant market movement, strengthen investment theses and enhance their trading strategies. Prattle is a part of the new wave of machine learning and AI that the future of finance is building upon.
We have a track record of hiring amazing people and giving them (experienced professionals and recent grads alike) autonomy and responsibility from the start. Our team runs the gamut from Ph.D.’s to junior developers, united by the desire to solve hard problems with data and creative solutions. You’ll have the best tools and resources available to get the job done and thrive in a collaborative environment where our teams are passionate about building great things.
RESPONSIBILITIES
- Analyze unique proprietary data sets.
- Create, evaluate, and enhance mathematical models and streaming analytics pipelines.
- Write production code.
- Partner globally with other data scientists, quants, developers, product designers, clients and industry leaders to tackle the important problems faced by the world’s largest asset managers.
SKILLS
- Currently studying Statistics, Economics, Quantitative Finance, Applied Physics, Engineering or related data analysis focused fields.
- Proficiency in a mainstream programming language, such as Python, R, C/C++, Go, or other languages with applicability to managing data and data analysis.
- Experience working with databases, preferably with knowledge of SQL.
- A strong foundation in a data driven/numeric discipline, including STEM, or quantitative social sciences.
- Familiarity with software development practices such as version control (git), test frameworks, code review tools, deployment configuration management, continuous integration.
- Experience with data analysis and machine learning libraries/frameworks and methods.
- Excellent communication skills and team orientation
- Ability to collaborate globally; leveraging technology to communicate and partner with our NY and London offices