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Internship Time Series Forecasting Jobs (NOW HIRING)

Inherent Technologies

Data Scientist

Chandler, AZ · Remote

Strong Time Series forecasting, ML, deep learning and standard statistical methods to evaluate models. Experience working on supply chain projects. We are seeking a highly skilled Data Scientist to ...

Gyrus AI

AI / Machine Learning Engineer

Santa Clara, CA · On-site

$80 - $120/hr

Minimum 2 years experience designing and implementing time series forecasting. * Minimum 2 years experience designing and implementing machine learning solutions including supervised and unsupervised ...

Techvilla Solutions

Data Scientist

Addison, TX

Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.

Techvilla Solutions

Data Scientist

Sunnyvale, TX

Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.

Techvilla Solutions

Data Scientist

Lancaster, TX · On-site

Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.

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

As of Aug 14, 2026, the average hourly pay for internship time series forecasting in the United States is $19.86, according to ZipRecruiter salary data. Most workers in this role earn between $17.07 and $22.36 per hour, depending on experience, location, and employer.

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

AspectInternship Time Series ForecastingData Analyst
Required CredentialsBasic knowledge of statistics, programming, and time series conceptsBachelor's degree in data-related fields, some roles may require certifications
Work EnvironmentInternship setting, often in finance, retail, or tech companiesFull-time or part-time roles in various industries, including finance, healthcare, and marketing
Employer & Industry UsageUsed for entry-level training and project support in forecasting tasksUsed for data analysis, reporting, and decision-making across industries

Internship Time Series Forecasting focuses on entry-level, project-based work involving forecasting models, while Data Analysts perform broader data analysis tasks, including reporting and insights. Both roles require analytical skills but differ in scope and experience level.

What skills and qualifications are needed for an internship in time series forecasting?

To thrive as an Internship Time Series Forecasting, you need a solid background in statistics, data analysis, and programming, often supported by coursework or experience in mathematics, economics, or computer science. Familiarity with statistical software and programming languages such as Python or R, as well as tools like pandas, NumPy, and forecasting libraries (e.g., Prophet, ARIMA), is typically required. Strong problem-solving skills, attention to detail, and effective communication set candidates apart in this analytical role. These abilities are crucial for accurately analyzing data trends, communicating insights, and delivering reliable forecasts that support business decisions.

What is an internship in time series forecasting?

An Internship in Time Series Forecasting is a temporary position that allows students or recent graduates to gain hands-on experience analyzing and predicting data points over time. Interns typically work with historical datasets to identify trends, seasonality, and patterns, often using statistical or machine learning models. These internships provide valuable exposure to real-world forecasting challenges in industries such as finance, retail, or technology, and help interns develop both technical and analytical skills. Interns may also collaborate with data scientists and business analysts to support decision-making processes.

What do interns in time series forecasting do?

As an intern in time series forecasting, you can expect to work on projects involving the analysis and modeling of sequential data—such as sales figures, stock prices, or sensor readings. Common tasks include cleaning and visualizing time-based datasets, applying statistical and machine learning models (like ARIMA or LSTM), and evaluating model performance. Interns often collaborate with data scientists and analysts to interpret results, present findings, and help integrate forecasting models into business processes. These experiences provide valuable hands-on exposure to both the technical and collaborative aspects of data science.
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Internship Time Series Forecasting jobs near you
Infographic showing various Internship Time Series Forecasting job openings in the United States as of August 2026, with employment types broken down into 12% Internship, 63% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $41,299 per year, or $19.9 per hour.

DATA SCIENTIST II-FINANCIAL & TIME SERIES FORECASTING

VSolvit

Norco, CA • On-site

$95K - $120K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Posted 4 days ago

Job description

*100% ON-SITE Norco, CA*

Job Summary

We are seeking a talented and driven Data Scientist to join our evolving and dynamic team. This position is open to candidates at Mid to Senior level of experience who possess a strong mathematical foundation and a passion for predictive modeling. In this role, you will build, validate, and maintain multi-variable time series forecasting models to project financial data and agency expenditures over multi-year horizons. You will work directly with financial records, taking ownership of the data lifecycle, from scraping, joining, and transforming raw datasets to rigorous model validation. We are looking for a resourceful problem-solver who excels at mathematical modeling, adapts quickly to changing data environments, and ensures our predictive tools remain accurate and resilient.


As with any position, additional expectations exist. Some of these are, but are not limited to, adhering to normal working hours, meeting deadlines, following company policies as outlined by the Employee Handbook, communicating regularly with assigned supervisor(s), and staying focused on the assigned tasks including company meetings, and completing other tasks as assigned.

Responsibilities

  • Time Series & Mathematical Modeling: Independently develop, compare, validate, and maintain advanced single-variable and multi-variable forecasting models using methods such as ARIMA, Prophet, regression, exponential smoothing, and tree-based models.
  • Data Pipeline Construction & Scripting: Design and maintain modular Python scripts and data pipelines to scrape, extract, clean, join, validate, and transform structured and unstructured financial data.
  • Model Validation & Quality Assurance: Design and execute backtesting, time-based cross-validation, regression testing, and model-monitoring workflows to ensure accuracy when code or data inputs change.
  • Quantitative Feature Engineering: Analyze historical pricing, inflation indices, economic indicators, budget cycles, and spending patterns to develop statistically and economically defensible features.
  • Model Performance & Risk Analysis: Evaluate forecast performance using RMSE, MAE, MAPE, bias, benchmark comparisons, and prediction intervals while preventing data leakage and look-ahead bias.
  • Technical Ownership: Document model assumptions, limitations, data dependencies, validation results, and recommended uses of model outputs.
  • Stakeholder Communication: Translate complex modeling results into clear reports, visualizations, recommendations, and leadership briefings.
  • Technical Guidance: Provide technical support, code reviews, and guidance to junior Data Scientists or analysts as assigned.
  • Resourcefulness & Learning: Evaluate new forecasting, statistical, AI, and machine-learning approaches and recommend improvements to analytical workflows.
  • Compliance & Security: Maintain strict compliance with defense security protocols, confidentiality guidelines, and internal data handling policies.
Basic Qualifications
US Citizenship Required
Ability to obtain and maintain a Secret Security Clearance
  • Bachelor’s degree in Mathematics, Statistics, Computer Science, Data Science, Economics, Finance, Engineering, or a related quantitative field and at least three years of relevant professional experience.
  • A Master’s or Ph.D. degree in a related quantitative field may substitute for a portion of the professional experience requirement.
  • Strong mathematical foundation in linear algebra, calculus, probability, statistics, and hypothesis testing.
  • Professional proficiency in Python for data manipulation, statistical modeling, time series forecasting, and automation scripting.
  • Demonstrated professional experience developing and validating predictive or time series forecasting models.
  • Demonstrated knowledge of both single-variable and multi-variable forecasting methods.
  • Experience with backtesting, time-based cross-validation, model benchmarking, and error-metric evaluation.
  • Strong understanding of data leakage, look-ahead bias, overfitting, feature stability, and model uncertainty.
  • Experience developing clean, modular, documented, and maintainable analytical code.
  • Ability to independently investigate data-quality, pipeline, or model-performance issues.
  • Strong verbal and written communication skills with the ability to explain mathematical models, assumptions, risks, and results to non-technical stakeholders.
  • If applicable: If you are or have been recently employed by the U.S. government, a post-employment ethics letter will be required if employment is offered.
Preferred Skills and Qualifications
  • Master’s or Ph.D. degree in Mathematics, Statistics, Computer Science, Data Science, Economics, Finance, Engineering, or a related quantitative field.
  • Professional experience in Economics, Finance, Econometrics, Quantitative Accounting, financial planning, or budget forecasting.
  • Experience forecasting financial expenditures, pricing trends, inflation-adjusted costs, or multi-year budget requirements.
  • Experience with scikit-learn, statsmodels, Prophet, XGBoost, LightGBM, or similar modeling libraries.
  • Experience with SQL, APIs, web scraping, ETL processes, databases, and automated data pipelines.
  • Experience working with government financial data or in a defense-related environment.
  • Experience with automated testing, model monitoring, reproducible analytical workflows, or production model deployment.
  • Experience with DevOps practices, Git, GitHub, CI/CD pipelines, containers, or cloud environments.
  • Experience preparing technical documentation, customer briefings, and leadership presentations.
  • Experience reviewing code or mentoring junior technical staff.
  • Continued education and knowledge of current and emerging AI/ML technologies.
  • Strong problem-solving skills and the ability to work independently in a fast-paced environment.


Company Summary

Join the VSolvit Team! Founded in 2006, VSolvit (pronounced 'We Solve It') is a technology services provider that specializes in cybersecurity, cloud computing, geographic information systems (GIS), business intelligence (BI) systems, data warehousing, engineering services, and custom database and application development. VSolvit is an award winning WOSB, CA CDB, MBE, WBE, and CMMI Level 3 certified company. We offer a customizable health benefits program that best meets the needs of its employees. Offering may include: medical, dental, and vision insurance, life insurance, long and short-term disability and other insurance products, Health Savings Account, Flexible Spending Account, 401K Retirement Plan options, Tuition Reimbursement, and assorted voluntary benefits. Our goal is to grow together and enjoy the work that we do as a team.


VSolvit LLC is an Equal Opportunity/Affirmative Action employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, national origin, protected veteran status, or disability status