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Bloomberg Machine Learning Jobs in New York (NOW HIRING)

... and machine learning models Excellent communication skills with ability to translate technical ... Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture ...

New

ML Engineer, Audio

New York, NY · On-site

$180K - $250K/yr

Stream has been featured in WSJ, Bloomberg, & Wired, and begins shipping in Summer '26. Join us in creating technology that extends human thinking. About We're looking for a machine learning engineer ...

... machine learning platform and decades of ML PhD expertise. Founded in 2022 and based in New York ... We're fortunate to have been featured in Wall Street Journal, Bloomberg, and American Banker ...

Head of Engineering

New York, NY · On-site

$230K - $300K/yr

... WSJ, Bloomberg, & Wired, and begins shipping in Summer '26. Join us in creating technology that extends human thinking. About As Head of Engineering, you'll lead our software and machine learning ...

Front Office Developer

New York, NY · On-site

$200K - $225K/yr

... machine learning techniques, mean-variance optimization, liquidity and execution analysis ... Python; utilizing Bloomberg, including BPipe and BLAAPI to generate alpha; performing data ...

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Bloomberg Machine Learning information

See New York salary details

$27.9K

$46.6K

$96.3K

How much do bloomberg machine learning jobs pay per year?

As of Jul 15, 2026, the average yearly pay for bloomberg machine learning in New York is $46,588.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,600.00 and $50,300.00 per year, depending on experience, location, and employer.

What is the difference between Bloomberg Machine Learning vs Bloomberg Data Analyst?

AspectBloomberg Machine LearningBloomberg Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; experience with ML frameworksDegree in Economics, Finance, or related; strong analytical skills
Work EnvironmentDeveloping algorithms, modeling, coding in Python/RData collection, analysis, reporting, using Excel/SQL
Industry UsageBuilding predictive models for financial dataInterpreting data trends for investment decisions

Bloomberg Machine Learning focuses on developing algorithms and models to analyze financial data, requiring programming and technical expertise. Bloomberg Data Analysts interpret and report on data trends, emphasizing analytical skills and financial knowledge. Both roles are integral to Bloomberg's data-driven environment but differ in technical depth and daily tasks.

What is a Bloomberg Machine Learning Engineer?

A Bloomberg Machine Learning Engineer is a specialist who develops and implements machine learning models and algorithms to solve complex financial problems using Bloomberg's vast datasets. They work closely with software engineers, data scientists, and business teams to improve data-driven products and services. Their responsibilities may include researching new machine learning techniques, optimizing existing models, and deploying solutions into Bloomberg's production systems. This role requires strong programming skills, experience with machine learning frameworks, and a solid understanding of financial markets.

How does a Machine Learning Engineer at Bloomberg typically collaborate with data scientists and software engineers?

At Bloomberg, Machine Learning Engineers work closely with data scientists to translate research models into production-ready systems, ensuring scalability and efficiency within real-time financial applications. They also partner with software engineers to integrate machine learning models into Bloomberg’s technology stack, maintaining performance and data security standards. Regular collaboration through agile methodologies and cross-functional meetings is common, allowing team members to align on project goals and address technical challenges quickly. This team-oriented environment fosters innovation and provides opportunities for skill development across both engineering and data science disciplines.

What are the key skills and qualifications needed to thrive as a Bloomberg Machine Learning Engineer, and why are they important?

To thrive as a Bloomberg Machine Learning Engineer, you need strong programming skills in Python or C++, a background in computer science or related field, and expertise in statistics and machine learning algorithms. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and version control systems like Git is essential, and advanced degrees or certifications in AI/ML are highly valued. Analytical thinking, problem-solving ability, collaboration, and effective communication are soft skills that set top performers apart. These competencies are crucial for building robust, scalable ML solutions that drive Bloomberg's data-driven products and maintain their industry-leading analytics.
What job categories do people searching Bloomberg Machine Learning jobs in New York look for? The top searched job categories for Bloomberg Machine Learning jobs in New York are:
Infographic showing various Bloomberg Machine Learning job openings in New York as of July 2026, with employment types broken down into 3% Locum Tenens, 91% Full Time, 5% Part Time, and 1% Contract. Highlights an 83% Physical, 7% Hybrid, and 10% Remote job distribution, with an average salary of $46,588 per year, or $22.4 per hour.
Senior Data Management Professional - Data Engineering - Commodities Data

Senior Data Management Professional - Data Engineering - Commodities Data

Bloomberg LP

New York, NY • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 10 days ago


Bloomberg rating

9.4

Company rating: 9.4 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

11th of 209 rated software companies


Job description

Senior Data Management Professional - Data Engineering - Commodities Data
Location
New York
Business Area
Data
Ref #
10050802
Description & Requirements
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 workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.
Our Team:
In the Commodities Data Team, we're responsible for onboarding, modelling and maintaining data that are fit for purpose for our clients. More than 320,000 business leaders rely on the real time financial information available on the Bloomberg Professional Service. Our products run on intelligence and insight provided by the Commodities Team. Our team of analysts provide valuable data and insights to key decision makers within the commodity markets.
We are responsible for the data management of datasets across Power and Gas, Oil, Carbon, Agriculture and Metals. The team provides relevant, timely and accurate data to empower customers to drive their analysis of commodity markets, both pricing and fundamentals.
The Role:
The Commodities Data team is looking for a highly experienced Senior Data Management Professional to help lead the next generation of our data platform. This role requires a strong data engineering foundation combined with deep ownership of data quality, where quality is built directly into pipelines, systems and architecture rather than managed as a separate function. This role is designed for a top-tier individual contributor who thrives in complex environments and consistently delivers high-impact, scalable solutions.
You will be responsible for designing and evolving data systems that power Tier 1 datasets, improving reliability, reducing technical debt and modernizing legacy workflows. This includes building advanced ETL pipelines, implementing intelligent automation and developing robust data quality controls and monitoring frameworks to ensure data accuracy, completeness and timeliness.
In addition, you will play a key role in defining and executing the data quality vision for our datasets. This includes evolving fit-for-purpose quality metrics, understanding how clients consume data across Bloomberg products and aligning data with both client needs and Bloomberg's commercial strategy. You will also influence data governance practices and lifecycle management across teams to ensure long-term data integrity and scalability.
You will collaborate closely with Product, Engineering and domain experts to define and execute on strategic data initiatives. In addition to hands-on development, you will act as a technical leader within the team by owning end-to-end solutions, influencing architecture decisions and mentoring others.
We are looking for someone who operates at a high bar of technical excellence, takes ownership of both data systems and data quality outcomes, and leverages modern technologies including AI and machine learning to enhance data workflows and extract additional value from our datasets.
We'll Trust You To:
- Build and maintain highly scalable, resilient and observable data pipelines supporting critical Commodities datasets
- Lead the modernization of legacy workflows, reducing technical debt and improving maintainability and performance
- Perform deep data analysis including profiling and root cause analysis to support data-driven decision making and validate improvements
- Build and deploy automated data quality controls, including anomaly detection and proactive monitoring.
- Apply AI and machine learning techniques such as natural language processing, entity extraction and anomaly detection to improve data ingestion and enrichment
- Identify opportunities to leverage generative AI and automation to reduce manual workflows and accelerate data onboarding
- Develop validation frameworks for agentic artifacts to ensure quality, reliability and appropriate controls.
- Own and drive large-scale data migrations and system redesigns
- Establish best practices around data architecture, pipeline design and workflow orchestration
- Understand how clients consume data across Bloomberg products and translate those needs into measurable data quality and product improvements
- Partner with Engineering to align on platform evolution, scalability and system design
- Act as a technical leader and mentor, raising the bar for code quality, design thinking and execution across the team
- Apply your proven project management expertise to ensure technical projects are aligned with requirements and stay on track
You'll Need to Have:
- A bachelor's degree or above in Statistics, Computer Science, Quantitative Finance or other STEM related field or degree-equivalent qualifications
- 4+ years of experience architecting, designing and implementing scalable data solutions and ETL pipelines, including monitoring, remediation and data management workflows across diverse data sources
- 4+ years of hands-on experience working with Python in development/production environment and working with databases either SQL/NoSQL
- Proven track record of owning and delivering complex, high-impact data initiatives end-to-end
- Strong experience with distributed data systems, workflow orchestration and scalable architecture design
- Hands-on experience applying machine learning or AI techniques in data workflows such as classification, NLP, anomaly detection or LLM-assisted workflows
- Strong experience in data quality management, including defining metrics, performing root cause analysis and driving measurable improvements in data reliability
- Experience building observable systems with monitoring, alerting and data reliability frameworks
- Ability to analyze and refactor legacy systems and drive measurable improvements in performance and scalability
- Familiarity with various databases, schemas, modeling, as well as structured and unstructured formats (PDF, HTML, XBRL, JSON, CSV etc.)
- Strong communication and interpersonal skills, with the proven ability to influence technical direction, mentor team members, clearly communicate complex concepts and methodologies, and effectively collaborate across diverse and distributed teams
We'd Love to See:
- Advanced degree in a relevant subject and/or Certified Data Management Professional (CDMP, or working towards it)
- Experience in Bloomberg products, Bloomberg Terminal fluency and/or Bloomberg Data Workflows
- Demonstrated experience working with Commodities markets and products
- Experience productionizing AI or machine learning models within data platforms
- Track record of driving efficiency gains through automation and intelligent systems
- Strong understanding of data governance, lineage and metadata management at scale
- Hands-on project management experience with familiarity in JIRA and QlikSense
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.
Salary Range = 110,000 - 190,000 USD Annual + Benefits + Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
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About Bloomberg

Sourced by ZipRecruiter

Bloomberg runs on data. As the Data Management & Analytics team within Engineering, we support our organization's needs around managing data efficiently. The vision of the team is to build solutions that drive data quality, data dictionary, data stewardship, data lineage, reference, and master data management across various data domains (prospect, customer, vendor, material etc.). We partner with business teams across the organization in addressing their data needs and ultimately helping run business operations efficiently and make improved decisions.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1981