: Quant Scientist - CTO Data Science
731 Lexington Ave
New York, NY 10022

Employee Testimonials

Bloomberg Testimonial
Bloomberg Testimonial

Video

Who we are:

The Bloomberg CTO Office is the future-looking technical arm of Bloomberg L.P. We envision, design and prototype the next generation infrastructure, hardware and applications that interface
in all aspects of the company including financial products, broadcast and media, data centers, internal IT and our global network. We are passionate about what we do.

What we do:
The CTO Data Science department is a dynamic, collaborative and intellectually stimulating environment - the work is always exciting and the problems we tackle are never boring. From this department we guide the company's overall strategic direction for machine learning, natural language processing, and search throughout the entire business. We are transforming our business through these technologies as well as the insights we provide our customers across the global financial sector.

At Bloomberg, our systems ingest hundreds of billions of market data ticks and millions of curated news stories for financial players to process and make investment decisions. The CTO Data Science department's machine learning efforts enhance our clients' ability to find the right pieces of information that are necessary to succeed in their jobs.

What's in it for you:

You will be part of a newly formed team within the CTO Data Science department at Bloomberg. You will work alongside world-class talent to find innovative solutions to some of the most interesting problems in the Financial Industry. You will work closely with others in the CTO office, Engineering, the Quantitative Research group and the Product organization to learn about problems our clients face and develop cutting edge solutions to these problems. Your focus will be diving deep into alternative data to develop time series, forecasting models, and quant strategies leveraging state-of-the-art machine learning and advanced statistical methods.

You'll need to have:

  • 3+ years' experience working in Quant Finance (research, quantitative / automated trading or quantmental group).
  • Undergraduate or higher degree in Computer Science, Mathematics, Economics, Operations Research, Econometrics or other quantitative discipline. Advanced degree or equivalent experience preferred.
  • Ability to clearly communicate research findings to technical and nontechnical clients.
  • Experience with Python (preferred), R or C++, as well Spark, SQL or other distributed data processing technologies.
  • Experience with scientific computing, time series, panel data, etc..
  • 3+ years of hands-on experience with Machine Learning and Statistics on large, unstructured, data sets.
  • Strong background in statistics, optimization, econometrics and knowledge of financial markets.
  • We'd like to see:

  • Ability to tackle loosely defined problems and a strong disposition to dive deep while maintaining strategic perspective.
  • Comfortable handling multiple projects to solve varied problems working with multiple teams.
  • Empirical, independent, detail-oriented mindset.
  • Sense of ownership of his/her work, working well both independently and within a collaborative team.
Full-time

Employee Testimonials

Bloomberg Testimonial
Andrew
Analytics

I’ve been at Bloomberg for almost 5 years now and throughout my time in Analytics, I learned an immense amount about the Customer Service industry. Particularly, how clients interact with client service representatives and through technological means. It also connected me to many different departments throughout the organization, and that knowledge and network has helped me drive results involving multiple stakeholders.

Bloomberg Testimonial
Jingyi
Analytics

I get uncomfortable if I start feeling too comfortable at work. Luckily there is no shortage of exciting challenges here.

Bloomberg Testimonial
Sridhar
News

I really enjoy working on stories that allow me to collaborate with other Bloomberg reporters to reveal deep insights on the largest operators in the credit space.