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Overnight Machine Learning Quant Jobs in Arizona

... quantitative discipline; advanced degree preferred. * Strong technical foundation in statistics, predictive modeling, machine learning algorithms, and programming languages such as Python and SQL.

Quantitative Analyst I

Scottsdale, AZ · Hybrid

$57K - $63K/yr

We are looking for a curious, detail-oriented analyst to join the Quantitative Analytics team! This ... Coursework or project experience in statistical modeling, machine learning, or data visualization

Quantitative Analyst I

Scottsdale, AZ · On-site

$57K - $63K/yr

We are looking for a curious, detail-oriented analyst to join the Quantitative Analytics team! This ... Coursework or project experience in statistical modeling, machine learning, or data visualization

This role leverages predictive modeling, experimentation, machine learning, and workforce analytics ... a related quantitative field required; Master's degree preferred. * Minimum of 5 years of ...

This role leverages predictive modeling, experimentation, machine learning, and workforce analytics ... a related quantitative field required; Master's degree preferred. * Minimum of 5 years of ...

This role leverages predictive modeling, experimentation, machine learning, and workforce analytics ... a related quantitative field required; Master's degree preferred. * Minimum of 5 years of ...

Quantitative Analyst I

Scottsdale, AZ · Hybrid

$57K - $63K/yr

Corporate Finance Opportunity in Financial Services Quantitative Analyst I Location(s): Scottsdale ... machine learning, or data visualization Interest in automation, process improvement, and scalable ...

Quantitative Analyst I

Scottsdale, AZ · Hybrid

$57K - $63K/yr

Corporate Finance Opportunity in Financial Services Quantitative Analyst I Location(s): Scottsdale ... Coursework or project experience in statistical modeling, machine learning, or data visualization

Machine learning: using computers to improve as well as develop algorithms; * Statistical analysis: to understand and work around possible limitations in models. Education * Degree in quantitative ...

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Overnight Machine Learning Quant information

What is the difference between Overnight Machine Learning Quant vs Quantitative Researcher?

AspectOvernight Machine Learning QuantQuantitative Researcher
CredentialsAdvanced degrees in CS, Math, or Stats; programming skillsSimilar; advanced degrees often required
Work EnvironmentFinancial firms, hedge funds, trading desks; fast-paced, data-drivenFinancial institutions, research labs; analytical, research-focused
Industry UsageHigh-frequency trading, algorithmic strategiesMarket analysis, model development
Work HoursOvernight shifts aligned with trading hoursStandard business hours, flexible in some cases

While both roles involve quantitative analysis and programming, Overnight Machine Learning Quants focus on developing models for overnight trading strategies, often working overnight shifts. Quantitative Researchers typically conduct broader market research and model development during regular hours. The roles overlap in skills but differ mainly in work hours and specific application areas.

What are the most commonly searched types of Machine Learning Quant jobs in Arizona?

The most popular types of Machine Learning Quant jobs in Arizona are:

What cities in Arizona are hiring for Overnight Machine Learning Quant jobs?

Cities in Arizona with the most Overnight Machine Learning Quant job openings:

Data Scientist

Champions Funding LLC

Gilbert, AZ • On-site

Full-time

Posted 27 days ago


Job description

Description:

• Design, develop, and evaluate machine learning, statistical, and predictive models to solve complex business challenges across multiple departments.
• Apply modern artificial intelligence and machine learning techniques, including large language models (LLMs), generative AI, and advanced analytics, to automate processes, enhance decision-making, and generate business insights.
• Translate business objectives into well-defined analytical, statistical, and machine learning solutions that deliver measurable business value.
• Analyze large, complex datasets to identify trends, patterns, opportunities, and operational improvements.
• Partner with data engineering, IT, and business teams to develop scalable data pipelines and deploy machine learning models into production environments.
• Evaluate data quality, model performance, and AI system limitations while ensuring responsible, ethical, and practical implementation of predictive models.
• Present analytical findings, recommendations, and technical concepts clearly to executive leadership and both technical and non-technical stakeholders.
• Develop, monitor, and optimize predictive models, ensuring ongoing performance, accuracy, and reliability through continuous improvement.
• Stay current with emerging technologies, AI advancements, machine learning methodologies, and data science best practices to identify opportunities for innovation.
• Collaborate across departments to support strategic initiatives, business intelligence projects, forecasting, automation, and operational optimization.
• Maintain thorough documentation of models, methodologies, assumptions, and development processes to support transparency, reproducibility, and governance.
• Support ad hoc analytical projects and provide data-driven recommendations that improve business performance and operational efficiency.

Requirements:

• Bachelor's degree required in Mathematics, Data Science, Computer Science, Engineering, Physics, or another quantitative discipline; advanced degree preferred.
• Strong technical foundation in statistics, predictive modeling, machine learning algorithms, and programming languages such as Python and SQL.
• Demonstrated experience working with modern AI technologies, including deep learning, large language models (LLMs), generative AI, MLOps, or related machine learning frameworks.
• Experience developing, deploying, and maintaining machine learning models in production environments.
• Strong understanding of cloud computing platforms and modern data science tools and technologies.
• Ability to evaluate model performance, balance trade-offs between accuracy, interpretability, speed, and risk, and apply sound judgment in ambiguous situations.
• Experience communicating complex technical concepts to business leaders and collaborating effectively with cross-functional teams.
• Experience within financial services, mortgage lending, or other highly regulated industries preferred.
• Familiarity with model governance, model risk management, compliance, or regulatory frameworks is a plus.