Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes - all while providing platinum customer support to our clients.

Bloomberg runs on data, and in the Data department, we are responsible for acquiring, interpreting, and supplying data insights to our clients. Our Data teams work to collect, analyze, 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 and supports 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,” through promoting increased data discoverability, accessibility, appraisability, interoperability, and analysis-readiness.

As a core component of the Quality Methods & Insights (QMI) group, the Process Engineering (PE) team supports the design and optimization of operational processes for our data manufacturing pipelines. We are passionate about how our people and technology work in concert, leveraging a suite of powerful tools built by infrastructure engineers. In partnership with Data Quality and Business Intelligence, we advance the Data Management Lab's mission by empowering data teams with advances in observability, instrumentation, and analysis to meet the evolving needs of Bloomberg's clients.

We are currently undergoing a strategic evolution, shifting our focus from tactical workflow implementation to becoming a center of excellence for system-level optimization. This means moving beyond prescribed solutions to fundamentally re-imagining how our data manufacturing pipelines operate. We are seeking a seasoned Industrial Engineer to be a driving force in this transformation, applying advanced methodologies from Operations Research, Industrial Engineering, and data analysis to solve our most complex challenges in quality, efficiency, and scale.

This is not a traditional process improvement role. You will be a strategic partner, a consultant, and an innovator, responsible for architecting the operational processes and frameworks that power the next generation of our intelligent, data-driven financial market content operations.

What You'll Be Trusted To Do:

• Conduct deep-dive investigations into our complex data manufacturing systems. You will go beyond surface-level metrics to identify systemic bottlenecks, anti-patterns, and latent inefficiencies that hinder quality and performance.
• Wield a full toolkit of advanced IE & OR methodologies—from Value Stream Mapping and Process Mining to understand end-to-end workflows, to Statistical Process Control for quality monitoring, to OR models (e.g. queuing theory and optimization) for solving complex resource challenges.
• Partner with data scientists and analytics engineers to rigorously analyze operational data, translating your findings into high-leverage opportunities for automation, waste reduction, and throughput improvement.
• Design and validate tractable discrete-event simulation models to forecast the impact of process changes, de-risk investments in new technology, and establish frameworks to measure automation ROI.
• Architect future-state operational frameworks by collaborating with data, product and engineering teams on conceptual designs for new tools, ensuring proper system instrumentation for data-driven control, and driving centralized process governance and innovation.
• Build trust and ongoing relationships with product-aligned teams to understand their business needs and identify strategic opportunities to advance business goals.
• Identify common automation opportunities, particularly leveraging AI for data processing and orchestration, to minimize waste and reduce low-value human effort.

You’ll need to have a strong combination of the following:

• An advanced degree (MSc/PhD) in Industrial Engineering, Operations Research, or a related quantitative field, combined with 5+ years of professional experience. A PhD may be considered in lieu of some professional experience, and we will also consider exceptional candidates with extensive experience and demonstrable portfolio of relevant work.
• Deep expertise across a range of IE & OR methodologies (e.g., VSM, SPC, process mining, simulation, optimization) and a proven track record of applying these theoretical models to solve complex, real-world problems.
• Strong technical proficiency in analyzing large datasets using SQL and a scripting language (Python is preferred).
• A first-principles, data-driven approach to problem-solving, with the critical thinking and abstraction skills to lead complex process analysis studies, particularly for human-in-the-loop systems.
• Excellent communication, interpersonal, and project management skills, with the ability to manage multiple global projects and collaborate effectively across diverse, cross-functional teams.
• Curiosity in keeping up with industry trends across technology, data, finance, productivity, and emerging fields like Machine Learning and Artificial Intelligence (AI), with an emphasis on incorporating AI into task distribution systems.

We’d love to see:

• Experience with workflow management systems like Jira.
• Lean Six Sigma Green or Black Belt certification (or analogous credentials).
• Contributions to thought leadership via white papers, conference proceedings, or journal articles.
• Experience in designing and analyzing human-in-the-loop or service-oriented systems.
• Experience in upskilling and knowledge sharing within an organization, promoting best practices and fostering a collaborative learning environment.
• Project Management Professional (PMP), Scrum Master certification (CSM), or other relevant professional certifications.
• Understanding of Data Governance and Data Management, supported by industry certifications (e.g., DAMA CDMP, DCAM).