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Quant Python Remote Jobs in Bellflower, CA (NOW HIRING)

Senior Engineer - LLMOps & MLOps

Los Angeles, CA · On-site +1

$112K - $154K/yr

Master's degree in a quantitative discipline highly desirable. Proven Execution: 6+ years of ... Expert Python, SQL, and PySpark. Extensive experience with containerization (Docker, Kubernetes ...

Posting Type Remote/Hybrid Job Overview WHO WE ARE Relativity is a leading legal data intelligence ... Strong Python programming skills and experience with common data and ML libraries such as numpy ...

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Quant Python Remote information

See Bellflower, CA salary details

$13

$61

$90

How much do quant python remote jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for quant python remote in Bellflower, CA is $61.26, according to ZipRecruiter salary data. Most workers in this role earn between $50.48 and $69.57 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Quant Python Remote professional, and why are they important?

To thrive as a Quant Python Remote professional, you need a strong background in quantitative analysis, mathematics, and expertise in Python programming, often supported by a degree in a quantitative field. Familiarity with libraries like NumPy, pandas, and scikit-learn, as well as experience with version control systems and cloud-based collaboration tools, is typically required. Strong problem-solving abilities, attention to detail, and effective remote communication skills help distinguish top performers in this role. These competencies are crucial for developing robust quantitative models, collaborating efficiently across distributed teams, and driving data-driven decision-making in finance or related sectors.

What is a Quant Python Remote job?

A Quant Python Remote job involves working as a quantitative analyst or developer, focusing on financial modeling, data analysis, and algorithmic trading using Python, all while working remotely. Professionals in this role use Python to develop quantitative strategies, analyze financial data, and create tools for risk management or trading. These jobs are popular in hedge funds, investment banks, and fintech companies seeking experts who can work from anywhere. Strong programming skills, knowledge of statistics, and experience in finance are typically required.

What is the difference between Quant Python Remote vs Quantitative Analyst?

AspectQuant Python RemoteQuantitative Analyst
Required CredentialsDegree in Math, Stats, or CS; Python proficiency; sometimes certificationsDegree in Finance, Math, or Economics; strong programming skills; certifications like CFA are common
Work EnvironmentRemote, flexible hours, often self-directedTypically office-based, but increasingly remote; collaborative teams
Employer & IndustryFinancial firms, hedge funds, fintech companiesInvestment banks, asset management firms, hedge funds
Search & Comparison IntentLooking for remote Python-based quant rolesSeeking quantitative analysis roles in finance

While both roles involve quantitative skills and finance knowledge, Quant Python Remote emphasizes remote work and Python programming, whereas Quantitative Analyst roles may be more traditional and office-based, often requiring finance-specific certifications. Candidates should consider their preferred work environment and skill set when choosing between these roles.

What are some typical challenges faced by Quant Python professionals working remotely, and how can they be addressed?

Quant Python professionals working remotely often encounter challenges such as collaborating effectively with team members across different time zones, maintaining clear communication on complex quantitative models, and ensuring secure access to sensitive financial data. To address these issues, it's important to utilize robust collaboration tools (like Slack or Zoom), establish regular check-ins with teammates, and follow best practices for code documentation and version control. Additionally, many employers provide secure VPNs and cloud-based platforms to facilitate safe data access, helping remote quants stay productive and connected.
What are popular job titles related to Quant Python Remote jobs in Bellflower, CA? For Quant Python Remote jobs in Bellflower, CA, the most frequently searched job titles are:
What job categories do people searching Quant Python Remote jobs in Bellflower, CA look for? The top searched job categories for Quant Python Remote jobs in Bellflower, CA are:
What cities near Bellflower, CA are hiring for Quant Python Remote jobs? Cities near Bellflower, CA with the most Quant Python Remote job openings:

Machine Learning Engineer (Hybrid- Greenfield Opportunity)

Match Made Tech

Irvine, CA • On-site, Remote

$75 - $95/hr

Other

Posted 3 days ago


Job description

AI/ML Engineer - Greenfield AI Project

UNABLE TO OFFER SPONSORSHIP- US CITIZENS & GREEN CARD ONLY

LOCATION: Irvine, CA (onsite). Monday through Thursday onsite, Fridays remote.

SPONSORSHIP NOT AVAILABLE- MUST BE US CITIZEN/ GREEN CARD HOLDER

COMPENSATION: $75-95 an hour. This is a 2-year contract that will convert to full-time.

About Us

We are on a mission to develop innovative AI solutions that will revolutionize our workforce. As we embark on an exciting new greenfield AI project, we are seeking an exceptional AI/ML Engineer to join our team and lead the development of machine learning models as part of this groundbreaking initiative.

Job Description

About the Role

We are seeking a skilled AI/ML Engineer to join our team to design, develop, and deploy machine learning models that solve real-world business challenges. You will work cross-functionally with data scientists, engineers, and product teams to bring cutting-edge AI solutions to production, with a strong focus on NLP, supervised learning, experimentation, and optimization.

Key Responsibilities
  • Model Development & Training
    • Collaborate with data scientists and stakeholders to translate project goals into scalable ML solutions.
    • Design, develop, and train models using state-of-the-art machine learning techniques and tools.
    • Select appropriate annotated datasets and transform raw data into machine learning-ready formats.
  • Data Preparation & Feature Engineering
    • Analyze and process structured/unstructured data for training and evaluation.
    • Develop feature extraction and selection pipelines to improve model performance.
  • Experimentation & Optimization
    • Run controlled experiments and perform statistical analysis to validate models.
    • Refine model hyperparameters and evaluation metrics for optimal performance.
  • Deployment & Integration
    • Work closely with ML Ops to deploy and monitor models in production environments.
    • Ensure all models are integrated seamlessly into existing systems.
  • Collaboration & Code Quality
    • Participate in code reviews, pair programming, and knowledge-sharing sessions.
    • Write testable, production-quality code that aligns with engineering best practices.
Qualifications & Skills
  • 3–5 years as an ML/AI Engineer or 1–3 years in an ML/AI leadership role
  • Proven experience building and deploying machine learning models in production
  • Solid understanding of classical ML algorithms (classification, regression, clustering)
  • Experience working with changing datasets and real-time data pipelines
  • Hands-on experience with Python and frameworks like PyTorch, TensorFlow, Scikit-learn
  • Strong knowledge of data processing (ETL), feature engineering, and statistical evaluation
  • Solid understanding of REST APIs, CI/CD, and containerized deployments (Docker, Kubernetes)
  • Strong communication, analytical thinking, and problem-solving skills
  • Bachelor's degree in Computer Science, Mathematics, Engineering, or a related quantitative field
Preferred Qualifications (Nice-to-Have)
  • Master's or PhD degree in Computer Science, Engineering, or a related field
  • Experience with neural networks and deep learning applications in computer vision, time-series analysis, or reinforcement learning
  • Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker, etc.)
  • Exposure to cloud platforms (AWS, GCP, Azure)
  • Familiarity with version control and experimentation tracking tools
  • Basic knowledge of data governance, security, and compliance standards