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 a collaborative set of teams, Quality Methods & Insights 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 leading initiatives to appraise and enhance the quality of our datasets, partnering closely with Data product and Engineering teams to champion effective solutions. Simultaneously, you will leverage your analytical expertise to support the development of scalable methods and tools for analysing product, process, and people data. The analytical insights 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.
As a valued member of our team, we’ll trust you to:
- Lead global initiatives focused on data science applications within the realms of data quality, data product development, and operational efficiency.
- Design and run studies to uncover root causes of data quality issues, using techniques such as hypothesis testing, clustering, and regression analysis.
- Develop statistical models to detect data anomalies, predict quality issues, and optimize data manufacturing pipelines by leveraging appropriate methodologies.
- 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; mentor others in applying best practices in modelling and experimentation.
You’ll need to have a strong combination of the following:
- A PhD or Master’s degree in Data Science, Economics, Statistics or a related quantitative field.
- 3+ years’ experience designing research studies as well as performing analysis such as data profiling, predictive modelling, and causal analysis.
- Strong coding skills ideally in Python and experience with SQL for data querying.
- Familiarity with version control systems (e.g., Git) and a collaborative development workflow (e.g., GitHub, GitLab).
- Experience working in a data quality, data governance, or data management environment is a major plus (knowledge of DAMA, DCAM, etc. is welcome).
- Excellent project management skills and the ability to communicate complex findings clearly to both technical and non-technical audiences.
- Knowledge of financial markets and Bloomberg products is a plus.
Does this sound like you? Apply if you think we’re a good match. We’ll get in touch to let you know what the next steps are.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Management Professional - Data Science - Data Management Lab | London, UK
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Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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How to prepare for a job interview at Bloomberg
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Bloomberg!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
