Applied Machine Learning Scientist - Data Technologies

Who we are:
The Bloomberg Engineering Data Technologies Department engineers systems and models that serve billions of data points to some of the World's most discerning customers each day. The Data Science team is a dynamic, collaborative and intellectually stimulating bunch, working together on interesting problems to bring impact to our customers and clients. We care deeply about building a diverse, inclusive organization, and hope that you can join us in this exciting work!

What we do:
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. Our company-wide machine learning efforts enhance our clients' ability to find the right pieces of information that are necessary to succeed in their jobs.

In our Data Science team, we are transforming the technology, the data and the insights that Bloomberg provides to our customers across the global financial sector, using data science and machine learning.

What's in it for you:
You will work alongside extraordinary talent to find innovative solutions to some of the most interesting problems in the Financial Industry (and beyond!) using data science and machine learning. In collaboration with colleagues in Engineering, the CTO office, the Data group and the Product organization, you will be working with complex datasets, developing prototypes to test analytical hypotheses and performing statistical quantitative analyses to drive business value for the company.

You'll need to have:

• 4+ years of experience with an object-oriented programming language such as Python or Java
• Subject matter expertise in one or more of the following: Artificial Intelligence (AI), Natural language Processing (NLP), Machine Learning (ML), Statistical Models, and Text Analytics on large data sets
• Master's or PhD in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience
• Experience using Apache Spark or other distributed data processing technologies
• Ability to work with the Software Development Lifecycle (version control, unit testing, etc.)

We'd like to see:

• Prior work with financial data in large-scale, data-rich environments
• Fluency in time series, panel data, behavioral analysis, text processing and/or alternative data and Bayesian statistics
• Experience with deep learning
• Sense of ownership of the work, working well both independently and collaboratively

Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law.

Bloomberg is a disability inclusive employer. Please let us know if you require any reasonable adjustments to be made for the recruitment process. If you would prefer to discuss this confidentially, please email amer_recruit@bloomberg.net.

Salary Range: 160,000 - 250,000 USD Annually + Benefits + Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation [Exempt roles only], paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.