Senior Data Management Professional - Data Science - Data Management Lab

Bloomberg L.P.; London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading financial services firm is seeking a Senior Data Management Professional in London to enhance data quality and drive impactful business decisions. Candidates should hold a PhD or Master’s degree in a quantitative field and have over three years of experience in data analysis and strong coding skills in Python and SQL. This role emphasizes collaboration and innovation in data-driven decision-making.

Qualifications

• 3+ years' experience in designing research studies and analysis.
• Experience in data quality, governance, or management.
• Excellent communication skills for technical and non-technical audiences.

Responsibilities

• Lead global initiatives in data science applications.
• Design studies to resolve data quality issues.
• Deliver actionable insights through advanced analytics.

Skills

Data profiling

Predictive modelling

Causal analysis

Strong coding skills in Python

SQL for data querying

Excellent project management skills

Education

PhD or Master's in Data Science, Economics, Statistics

Tools

Git

GitHub/GitLab

Job description

Senior Data Management Professional - Data Science - Data Management Lab

Location

London

Business Area

Data

Ref #

10043689

Description & Requirements

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 partner 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:

*Please note we use years of experience as a guide but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.

• 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.

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 [e-mail]