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Applied Data Analyst - Data Quality Analytics

$110k - $225k

Bloomberg

Applied Data Analyst - Data Quality Analytics


Location


New York


Business Area


Product


Ref #


10048633


Description & Requirements

We are Bloomberg Enterprise Data - fast paced, innovative and expanding! We partner closely with our clients, taking time to understand their unique businesses and individual data and financial technology (FinTech) needs. Our endless selection of datasets, covering all asset types, with multiple delivery technologies and flexible scheduling enables our clients to get exactly the data they need, when they need it, in the format they prefer. Put simply, our award-winning data powers the capital markets.


We are looking for a Product Analyst in Enterprise Data to closely interact and work with various stakeholders to be the gatekeeper and ensure the data quality for product development, launch and enhancement. You will use your quantitative skills, knowledge of finance, and technical skills to conduct data analysis, anomaly detection for our most innovative products. This includes, but is not limited to reference & pricing, company fundamentals, sustainable finance solutions, Risk & Investment analytics products and other Bloomberg Data License products.


We'll trust you to:

  • Conduct time series data anomaly detection for patterns in existing product, provide solution to detect abnormal trending in data (for example coverage drop, missing value, data change and ect), back testing model performance
  • Prepare exploratory data analysis including statistical and visual summaries, assess data and product quality, identify product weakness, and propose potential enhancements.
  • Communicate results to product managers and software developers
  • Collaborate with product, engineering and sales teams on product delivery, task prioritization and product roadmap
  • Answer client questions and address client feedback in product enhancements/bugfixes
  • Conduct product usage analysis to improve data product offerings
  • Contribute to scaling team activities through automation like monitoring daily production data generation.
You'll need to have:
  • 3+ years of experience working with model development for time series data anomaly detection
  • Bachelor's or Master's degree in quantitative discipline (financial engineering, statistics, computer science, finance, engineering, etc)
  • Hands on experience in analytical fields such as data science, quantitative analysis, data visualization, data mining, anomaly detection and data validation
  • Financial knowledge in different asset classes like equity, fixed income, mortgages, muni, futures, options
  • Attention to data integrity
  • Programming skills in Python and SQL
  • Statistics and mathematical knowledge to perform quantitative analysis
  • Experience gathering, matching, and pre-processing large data sets from varied sources and of different characteristics
Would love to see:
  • Experience working with clients, business managers or product managers in identifying their needs and taking proactive steps to effectively communicate project processes

Salary Range = 110,000 - 225,000 USD Annual + 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.

Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.
Vacancy posted 5 days ago
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