Bloomberg runs on data, and in the Data department we're responsible for acquiring, interpreting and supplying data insights to our clients. Our Data teams work to collect, analyse, process, and publish the data which is the backbone of our iconic Bloomberg Terminal -- the data which ultimately moves the financial markets! We're responsible for delivering this data, news, and analytics through innovative technology -- quickly and accurately.

The Data Management Lab (DML) sits within the Data organization, supporting Data's pursuit of data management excellence by aligning industry best practices with Bloomberg's established expertise in financial market data. DML empowers our data professionals to make their products "ready-to-use" by promoting increased data discoverability, accessibility, appraisability, interoperability, and analysis-readiness.

As a Data Management Professional, you will play a pivotal role in ensuring the delivery of high-quality data to our clients while driving impactful business decisions. You will be an integral member of Asia-Pacific Quality Methods & Insights team under DML that includes Data Quality, Business Intelligence and Process Engineering, serving as a centre of excellence for the rest of the teams in the Data organisation. A key aspect of this role involves partnering with Data Product and Engineering teams to conduct analysis of operational and product data that is characterized by high volume and variety, generated by a variety of sources in a complex production environment. Specifically, you will leverage your analytical expertise to support the development and adoption of methods that will directly support data-driven decision-making aimed at achieving quality enhancements and process optimisation across the organization. You will also contribute to the ongoing refinement of data management best practices.

We'll trust you to:

• Lead global initiatives within the realms of data quality, operational efficiency and data management
• Design and run analyses to uncover root causes for data quality issues and make recommendations using techniques in the areas of Bayesian analysis, machine learning, and causal inference
• Formulate and execute optimization analysis to balance trade-offs such as performance, quality and resource usage across data pipelines
• Bring exposure to Operations Research methods such as Linear programming, Queuing theory, and Monte Carlo Simulations
• Bring a basic understanding of data engineering workflows including how data pipelines are orchestrated and various components within the ETL framework
• Deliver actionable insights through advanced analytics, and compelling data storytelling to support business decision making and innovation
• Collaborate with data stakeholders and engineering partners to translate high-impact questions into scalable data science solutions
• Build statistical and analytical capabilities within the team and mentor others

You'll need to have:

• A Bachelor's degree or higher in Data Science, Economics, Statistics or a relevant STEM field, or equivalent professional work experience
• 1-2 years' experience designing data engineering pipelines
• Proficiency in Python (e.g. PySpark, Pandas, NumPy or Pymc) and SQL
• 3-5 years of experience working in a data quality, data governance, or data management role
• 3-5 years' experience designing research studies using predictive modelling, causal analysis and/or Operations Research methods
• Familiarity with modern data tech stack tools such as dbt, Iceberg, Trino, Airflow, Superset, etc.
• Familiarity with version control systems (e.g., Git) and a collaborative development workflow environment (e.g., GitHub, GitLab)
• Excellent project management skills and the ability to communicate complex findings clearly to both technical and non-technical audiences
• Demonstrated continuous career growth within an organization
• Excellent written and verbal communication skill in English

We'd love to see:

• Experience in Data Management Association (DAMA) and Data Management Capability Assessment Model (DCAM)
• Knowledge of financial markets and Bloomberg products is a plus