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Trade Error Analyst Jobs (NOW HIRING)

Senior Machine Learning Engineer

Houston, TX ยท On-site

$99K - $137K/yr

Develop predictive models for trading, risk assessment, and operational efficiency. * Collaborate ... Rigorously evaluate models using appropriate metrics and error analysis. Qualifications & Skills:

Conduct design of experiments and experimental error analyses. * Support large and small scale test ... trade studies, develop design requirements, support attributes of piece parts design and support ...

Conduct design of experiments and experimental error analyses. * Support large and small scale test ... trade studies, develop design requirements, support attributes of piece parts design and support ...

Conduct design of experiments and experimental error analyses. * Support large and small scale test ... trade studies, develop design requirements, support attributes of piece parts design and support ...

... error analysis to refine model quality. Create evaluation metrics and results that tie to business ... Communicate model results and trade-offs to leadership and stakeholder. You will come with

Timely and accurate resolution of all trade breaks and compliance reporting issues (OATS, ACT, etc ... Monitor error accounts, including running, covering, and documenting errors, as well as providing ...

Clearly articulate trade-offs among different modeling approaches and their business implications ... perform error analysis to improve model quality. Develop evaluation metrics tied to business ...

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Trade Error Analyst information

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How much do trade error analyst jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for trade error analyst in the United States is $39.96, according to ZipRecruiter salary data. Most workers in this role earn between $30.29 and $53.37 per hour, depending on experience, location, and employer.

What is a trade error analyst?

Trade Error Analysts are financial professionals responsible for identifying, investigating, and resolving discrepancies or mistakes that occur during the processing of trades in financial markets. They work closely with traders, operations teams, and compliance departments to ensure that all transactions are accurate and meet regulatory standards. Their goal is to minimize financial loss, prevent future errors, and maintain the integrity of trade records. They often analyze error trends to recommend process improvements and enhance operational efficiency.

What are the key skills and qualifications needed to thrive as a trade error analyst?

To thrive as a Trade Error Analyst, you need strong analytical skills, attention to detail, and a solid understanding of financial markets and trade processes, often supported by a degree in finance, accounting, or a related field. Familiarity with trade settlement systems (such as SWIFT or DTCC), trade reconciliation tools, and proficiency in Excel or other data analysis software is typically required. Excellent problem-solving abilities, effective communication, and the capacity to work under pressure are key soft skills for this role. These qualifications ensure accurate error identification and resolution, minimizing financial risk and maintaining regulatory compliance for financial institutions.

What are some common challenges a trade error analyst may face and how can they be addressed?

Trade Error Analysts often encounter challenges such as reconciling discrepancies under tight deadlines, swiftly identifying root causes of errors, and communicating effectively with both front and back office teams. To address these, strong attention to detail, a methodical approach to investigation, and solid knowledge of trading systems are essential. Building collaborative relationships with other departments and proactively participating in process improvements can also help minimize the occurrence of future errors and foster a more efficient workflow.

What are popular job titles related to Trade Error Analyst jobs?

For Trade Error Analyst jobs, the most frequently searched job titles are:

Infographic showing various Trade Error Analyst job openings in the United States as of September 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $83,119 per year, or $40 per hour.

Senior Machine Learning Engineer

Houston, TX โ€ข On-site

$99K - $137K/yr

Contractor

Re-posted 18 days ago


Job description

Job Title: Senior Machine Learning Engineer

Location: Houston, TX

Environment: Standard, 5-days onsite

Job Description :

 

Must-Have (Technical Expertise & Core Responsibilities)

  • Deep Neural Networks (DNN):
    • Hands-on experience with CNN, RNN, Graph Neural Networks, and transformers.
    • Proficiency in hyperparameter optimization, autoencoders, model evaluation, and error metrics.
  • Generative AI:
    • Strong knowledge of LLMs (BERT, GPT, etc.), embeddings, and supervised fine-tuning.
    • Experience with Reinforcement Learning, RAG (Retrieval-Augmented Generation), and Agentic AI.
    • Familiarity with GraphRAG and LLM-as-a-judge architectures.
  • Predictive Analytics:
    • Expertise in classification, regression, anomaly detection, and sequence modeling.
    • Practical application of NLP techniques (sentiment analysis, entity recognition) and knowledge graphs.

Core Responsibilities:

  • Design, train, and optimize DNN and generative models for real-world business problems.
  • Implement LLM-based solutions (fine-tuning, RAG, agents) to enhance decision-making.
  • Develop predictive models for trading, risk assessment, and operational efficiency.
  • Collaborate with teams to integrate AI/ML solutions into production systems.
  • Rigorously evaluate models using appropriate metrics and error analysis.

Qualifications & Skills:

  • Master’s/Ph.D. in Computer Science, ML, or related field.
  • 5-7+ years of industry experience in applying DNN, generative AI, and predictive analytics.
  • Python mastery (TensorFlow/PyTorch, Transformers, Scikit-learn).
  • Cloud (AWS) and containerization (Docker) experience.

Nice-to-Have (Preferred Experience):

  • Production experience with GenAI models in the energy/commodities trading sector.
  • Experience with interactive dashboards (Dash, Streamlit) and time series modeling.
  • Knowledge of data orchestrators (Airflow, Dagster) and CI/CD pipelines.