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Internship Algorithmic Trading Programmer Jobs in New Haven, CT

Ranpak

Automation Solutions Engineer

Shelton, CT · On-site

... algorithm tool) consulting. * Develop specifications, system descriptions, and performance ... Present solution concepts, technical trade-offs, and value propositions to both technical and ...

RANPAK

Automation Solutions Engineer

Shelton, CT · On-site

... algorithm tool) consulting. * Develop specifications, system descriptions, and performance ... Present solution concepts, technical trade-offs, and value propositions to both technical and ...

Ranpak

Automation Solutions Engineer

Shelton, CT · On-site

... algorithm tool) consulting. * Develop specifications, system descriptions, and performance ... Present solution concepts, technical trade-offs, and value propositions to both technical and ...

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All JobsAlgorithmic Trading Programmer JobsInternship Algorithmic Trading Programmer JobsInternship Algorithmic Trading Programmer Jobs in New Haven, CT

Internship Algorithmic Trading Programmer information

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$16

$37

$53

How much do internship algorithmic trading programmer jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for internship algorithmic trading programmer in New Haven, CT is $37.09, according to ZipRecruiter salary data. Most workers in this role earn between $30.72 and $43.51 per hour, depending on experience, location, and employer.

What is an internship algorithmic trading programmer?

Internship Algorithmic Trading Programmers are students or early-career professionals who work as interns in financial firms to help develop, test, and implement automated trading strategies using programming and quantitative analysis. They typically work under the supervision of experienced traders or quantitative analysts, using programming languages like Python, C++, or Java to code algorithms that trade financial instruments. These internships provide valuable hands-on experience in financial markets, algorithm development, and risk management, helping interns build the skills needed for a career in quantitative finance or trading technology.

What types of projects and tasks can I expect to work on as an internship algorithmic trading programmer?

As an Internship Algorithmic Trading Programmer, you can expect to work on tasks such as coding and backtesting trading algorithms, analyzing financial market data, and optimizing strategies for performance. Interns often collaborate closely with quantitative analysts and senior developers, learning industry-standard tools and platforms. Your daily responsibilities may include debugging code, running simulations, and documenting your findings. This hands-on experience provides excellent exposure to the fast-paced trading environment and is a valuable stepping stone to full-time roles in quantitative finance.

What are the key skills and qualifications needed to thrive as an internship algorithmic trading programmer, and why are they important?

To thrive as an Internship Algorithmic Trading Programmer, you need strong programming skills (typically in Python, C++, or Java), a solid grasp of algorithms, and foundational knowledge in mathematics or finance, often supported by ongoing studies in computer science, engineering, or related fields. Familiarity with version control systems (like Git), data analysis tools (such as Pandas or NumPy), and exposure to trading platforms or APIs is advantageous. Analytical thinking, attention to detail, and effective communication are crucial soft skills for collaborating with teams and troubleshooting complex problems. These skills ensure the development of robust, efficient trading algorithms and contribute to the competitiveness and reliability of trading strategies.

What is the difference between Internship Algorithmic Trading Programmer vs Quantitative Trading Intern?

AspectInternship Algorithmic Trading ProgrammerQuantitative Trading Intern
Required CredentialsBasic programming skills, coursework in algorithms or financeStrong math, statistics, or finance background
Work EnvironmentDeveloping trading algorithms, coding, testing strategiesData analysis, model development, research
Industry UsageCommon in trading firms, hedge funds, investment banksSimilar, often overlapping roles in finance firms

Both roles involve finance and programming, but the Internship Algorithmic Trading Programmer focuses more on coding and implementing trading algorithms, while the Quantitative Trading Intern emphasizes data analysis and quantitative research. The roles are closely related and often overlap in industry usage.

What are popular job titles related to Internship Algorithmic Trading Programmer jobs in New Haven, CT?

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Internship Algorithmic Trading Programmer jobs near you

Python Software Engineer - Financial Engineering

Risk Analytics Company

Guilford, CT • On-site

$100K - $205K/yr

Full-time

Posted 24 days ago

Job description

Job Title: Python Software Engineer – Financial EngineeringPosition Overview
We are an Portfolio Risk Analytics Company seeking a highly skilled Python Software Engineer with a strong background in financial engineering to design, develop, and maintain quantitative financial applications. The ideal candidate has experience building analytical tools, pricing models, trading systems, or risk management platforms using Python and modern software engineering practices.
Responsibilities
  • Design, develop, and maintain Python applications for financial analysis and quantitative modeling.
  • Build and optimize pricing, valuation, and risk management models for financial instruments.
  • Develop data pipelines for processing market, economic, and alternative data.
  • Implement and maintain backtesting frameworks for trading and investment strategies.
  • Collaborate with quantitative researchers, traders, portfolio managers, and software engineers.
  • Optimize code for performance, scalability, and reliability.
  • Integrate applications with market data providers, databases, and APIs.
  • Write clean, maintainable, and well-documented code.
  • Develop automated testing and deployment pipelines.
  • Monitor production systems and troubleshoot technical issues.
Required Qualifications
  • Bachelor's, Master's, PhD's degree in Computer Science, Financial Engineering, Mathematics, Physics, Engineering, or a related quantitative field.
  • 3+ years of professional Python development experience.
  • Strong knowledge of object-oriented programming and software design principles.
  • Experience with financial engineering concepts, including:
    • Derivative pricing
    • Fixed income analytics
    • Portfolio optimization
    • Risk management
    • Time series analysis
  • Experience with Python libraries such as:
    • NumPy
    • Pandas
    • SciPy
    • Statsmodels
    • scikit-learn
  • Experience working with SQL databases.
  • Familiarity with REST APIs and cloud platforms.
  • Experience using Git and CI/CD workflows.
  • Strong analytical and problem-solving skills.
Preferred Qualifications
  • Experience developing algorithmic trading systems.
  • Knowledge of stochastic calculus, Monte Carlo simulation, and numerical optimization.
  • Familiarity with financial data providers (S&P, Bloomberg, Refinitiv, ICE, Polygon.io, etc.).
  • Experience with distributed computing or high-performance computing.
  • Knowledge of Docker, Kubernetes, or cloud infrastructure (AWS, Azure, or GCP).
  • Experience with machine learning applied to financial markets.
  • Familiarity with C++, Rust, or Java is a plus.
Technical Skills
  • Python
  • NumPy
  • Pandas
  • SciPy
  • SQL
  • Git
  • Linux
  • Docker
  • REST APIs
  • Financial Modeling
  • Quantitative Finance
  • Risk Analytics
  • Time Series Analysis
Desired Personal Attributes
  • Strong quantitative reasoning
  • Excellent communication skills
  • Attention to detail
  • Ability to work independently and collaboratively
  • Passion for financial markets and technology
  • Commitment to writing high-quality, maintainable software
Nice-to-Have Experience
  • Quantitative research
  • Options pricing
  • Fixed income analytics
  • Portfolio construction
  • Market risk or credit risk systems
  • Backtesting platforms
  • Financial data engineering
  • AI/ML applications in finance