Finance jobs in United States

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 the Data department, 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 implement technology solutions to enhance our systems, products, and processes — all while providing platinum customer support to our clients.

At Bloomberg, our team is responsible for onboarding our junior data engineers, as well as providing learning opportunities to develop the skills of our nearly 2,000 Data employees. We collaborate with all teams across Data to ensure that we deliver the highest quality educational development. We also roll up our sleeves to create our own training and applied exercises. You can support our purpose by preparing Data teams for an AI-driven future by strengthening their analytical reasoning, statistical literacy, and confidence in applying AI tools responsibly and effectively.

We strive to make our curriculum exciting for both trainers and trainees; we use interactive technology, peer learning, and a highly collaborative team culture to ensure success for everyone. We encourage participation and provide opportunities for trainees to learn from each other and the professionals within Data.

• Design and deliver training on applied experimentation and causal reasoning that enables teams to evaluate process changes - such as adopting new data pipelines, switching validation methods, or implementing AI-assisted workflows - and quantify their impact on dataset quality and business outcomes.
• Build a curriculum on experimental design, A/B testing, and hypothesis testing for data operations and teach teams to run controlled experiments to quantify improvements based on workflow changes
• Design and deliver analytics and statistics training that strengthens quantitative reasoning, data quality assessment (accuracy, completeness, reliability), and AI-enhanced insight generation.
• Create hands‑on labs where teams design experiments on real Bloomberg datasets—testing pipeline changes, evaluating new tools, and measuring quality improvements using statistical methods.
• Explain core statistical concepts (sampling, correlation, causation, p-values) in the context of data quality and process optimization.
• Incorporate AI‑assisted tools (e.g., GitHub Copilot, ChatGPT, NotebookLM) into training design and delivery.
• Ensure teams maintain the highest standards for data quality, observability, and governance, alongside the implementation of transformative AI technologies.
• Create structured guides and reusable frameworks (experiment templates, statistical calculators, decision tools) that enable teams to independently design experiments and adopt new tools and scale impact across the organization.
• Partner with engineers and domain experts to ensure we’re meeting client needs and leveraging the best technology solutions.
• Develop self‑service materials that enable teams to independently design experiments and adopt new tools.
• Stay current with emerging experimentation methods, AI tools, and financial market dynamics—continuously refining curricula to meet evolving Data organization needs and business priorities.
• Commitment to cultivating a continuous learning culture across technical teams.

*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.

• 3+ years experience in data analytics or statistics with hands‑on experience designing and analyzing experiments (A/B tests, causal inference studies, process optimization trials) within data‑centric environments
• Bachelor’s degree or higher in Computer Science, Engineering, Data Science, or other data‑related field.
• Strong foundation in experimental design and statistical inference: hypothesis testing, confidence intervals, power analysis, p-values, correlation vs. causation, and when different methods apply
• Proficiency with statistical analysis in Python or R, including experimentation libraries (scipy, statsmodels, scikit‑learn) and data manipulation tools (Pandas, SQL)
• Experience mentoring or teaching technical material, with a passion for continuous learning and knowledge sharing. Ability to translate technical concepts into clear learning content and documentation.
• Strong communication and teaching abilities— a proven track record explaining complex quantitative concepts to both technical and non‑technical audiences through clear examples and hands‑on exercises.
• Ability to identify learning needs through stakeholder consultation and translate them into scalable, practical training solutions
• Understanding of data quality metrics (accuracy, completeness, timeliness) and concepts (data observability, governance) and how to assess them through statistical methods
• Proven problem‑solving skills and adaptability in evolving, fast‑paced environments.
• Collaborative approach to partnering across global teams and aligning with business priorities.
• Effective project management skills to develop and manage a roadmap and deliver milestones in a timely manner.
• Ability to flexibly adapt to a changing environment.
• Interest in financial market datasets and their application to data solutions.
• Familiarity with modern data tools and frameworks (e.g., Airflow, Dagster, dbt, Spark, cloud data platforms)
• Active engagement with professional or academic communities in data science, analytics education, or applied experimentation.
• Certification in DAMA CDMP, EDM DCAM or similar.
• Examples of technical content you've created—whether documentation, tutorials, presentations, or internal training materials
• Hands‑on experience with financial data, market data, or other business‑critical datasets

Apply if you think we're a good match! We'll get in touch to let you know what the next steps are.

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