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Internship Time Series Analysis Jobs (NOW HIRING)

Hp

Print Supplies Big Data Analyst

Vancouver, WA

$105K - $161K/yr

... time-series analysis, reproducible workflows, and tooling to support recurring quality checks and investigations SQL , including complex joins, CTEs, window functions, query performance optimization ...

General Atomics

Data Scientist III

Charlottesville, VA · On-site

Expertise in stochastic processes and time series analysis * Proficiency in Python, MATLAB, R, or ... Prior internships or collaborations with defense, aerospace, or robotics organizations * Experience ...

General Atomics

Data Scientist III

Charlottesville, VA · On-site

$98K - $171K/yr

Expertise in stochastic processes and time series analysis * Proficiency in Python, MATLAB, R, or ... Prior internships or collaborations with defense, aerospace, or robotics organizations * Experience ...

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How much do internship time series analysis jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for internship time series analysis in the United States is $15.54, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $17.55 per hour, depending on experience, location, and employer.

What types of projects or tasks can I expect to work on during an internship in time series analysis?

As an intern specializing in time series analysis, you will typically assist with collecting, cleaning, and analyzing temporal data using statistical software such as Python or R. Your projects may include forecasting trends, detecting anomalies, and visualizing patterns in data sets from fields like finance, healthcare, or operations. You will often collaborate with data scientists, analysts, and business stakeholders to translate findings into actionable insights, while learning to apply techniques such as ARIMA, exponential smoothing, or machine learning models. This hands-on experience is a valuable foundation for pursuing advanced roles in data science and analytics.

What is the difference between Internship Time Series Analysis vs Data Analyst?

AspectInternship Time Series AnalysisData Analyst
Required CredentialsRelevant coursework, basic statistical knowledgeBachelor's degree in related field, some certifications
Work EnvironmentInternship setting, supervised projectsFull-time or part-time professional role
Industry UsageEntry-level, learning-focusedBusiness, finance, healthcare, and more

Internship Time Series Analysis is an entry-level, learning-focused role typically performed during internships, emphasizing foundational skills in analyzing time-based data. In contrast, Data Analysts are more experienced professionals responsible for interpreting data to inform business decisions. While both roles involve data analysis, internships are more about gaining experience, whereas Data Analysts perform ongoing, complex analysis in various industries.

What are the key skills and qualifications needed to thrive as an internship time series analysis?

To thrive as an Internship Time Series Analysis, you need a solid grounding in statistics, data analysis, and proficiency in mathematical concepts, usually supported by coursework in mathematics, statistics, or data science. Familiarity with technical tools such as Python or R, and experience with libraries like pandas, NumPy, and statsmodels, as well as data visualization platforms, are highly valuable. Analytical thinking, problem-solving abilities, and strong communication skills set candidates apart in interpreting results and presenting findings. These skills ensure accurate modeling, effective data-driven insights, and clear communication of complex temporal patterns for impactful business or research decisions.

What is an internship in time series analysis?

An internship in time series analysis is a temporary position, often for students or recent graduates, where you gain hands-on experience analyzing data that is collected over time. Interns typically work with datasets to identify trends, patterns, and make forecasts using statistical and machine learning techniques. The role may involve using tools like Python, R, or specialized software to clean data, build models, and visualize results. It’s a valuable opportunity to apply theoretical knowledge from coursework to real-world problems in industries such as finance, economics, or technology.
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Internship Time Series Analysis jobs near you
Infographic showing various Internship Time Series Analysis job openings in the United States as of August 2026, with employment types broken down into 33% Full Time, and 67% Part Time. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $32,333 per year, or $15.5 per hour.

Python Software Engineer - Financial Engineering

Risk Analytics Company

Guilford, CT • On-site

$100K - $205K/yr

Full-time

Posted 11 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