Who we are:

At the Bloomberg Engineering Data Technologies Department we engineer and productionize the systems and models that serve billions of data points to some of the World’s most discerning customers each day.

What we do:

The Quant Data Science team is a dynamic, collaborative and intellectually stimulating environment - the work is always exciting and the problems we tackle are never boring. We are transforming 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 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 colleagues in Engineering, the CTO office, Engineering, the Quantitative Research group and the Product organization to learn about problems our clients face when investing 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 research leveraging state-of-the-art machine learning and advanced statistical methods.

You’ll need to have:

•  3+ years’ experience working in Buy-side environments (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..
•  5+ years of hands-on experience with Machine Learning and Statistics on large, unstructured, data sets.
•  Professional proficiency in Big Data languages such as Spark (scala or pyspark).
•  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.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.