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Forecasting Python Arima Time Series Jobs (NOW HIRING)

Data Scientist

$90K - $150K/yr

... time series models (including econometric approaches) for accurately forecasting demand 23-36 ... Experience in Python statistics and machine learning framework and libraries, including statsmodels ...

Data Scientist

$90K - $150K/yr

... time series models (including econometric approaches) for accurately forecasting demand 23-36 ... Experience in Python statistics and machine learning framework and libraries, including statsmodels ...

Hands-on forecasting expertise, including ARIMA, Prophet, or comparable time-series methods ... Python Proficiency * Statistical Judgment * A/B Testing Experience Hard Skills * Data Analysis

Deep proficiency in Python and SQL for data processing, feature engineering, and ML model ... time series methods including anomaly detection, forecasting, and signal processing (e.g., ARIMA ...

Time-Series Forecasting: Proven track record developing and deploying production forecasting models (ARIMA, SARIMAX, gradient boosting methods) * Production Deployment: Demonstrated experience taking ...

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How much do forecasting python arima time series jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for forecasting python arima time series in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What is a forecasting Python ARIMA time series?

A Forecasting Python ARIMA Time Series job involves using the ARIMA (AutoRegressive Integrated Moving Average) statistical model to analyze and predict future values in time series data using Python. Professionals in this role typically preprocess data, fit ARIMA models, evaluate model performance, and generate forecasts to help businesses make data-driven decisions. This position requires strong skills in Python programming, statistics, and time series analysis, often working with libraries like statsmodels and pandas.

What are some common challenges faced by professionals working with ARIMA models in Python for time series forecasting?

Professionals working with ARIMA models in Python often encounter challenges such as identifying the optimal order of the model (p, d, q), handling missing data or outliers, and ensuring data stationarity before modeling. Additionally, interpreting model diagnostics and implementing seasonal adjustments can require careful attention. Collaborating with data engineers and domain experts is also vital, as accurate forecasts often depend on context-specific knowledge and robust data pipelines.

What are the key skills and qualifications needed to thrive as a forecasting Python ARIMA time series analyst, and why are they important?

To excel as a Forecasting Python ARIMA Time Series Analyst, a strong background in statistics, time series analysis, and a relevant degree in mathematics, statistics, or computer science is essential. Expertise in Python programming, especially with libraries like statsmodels and pandas, and familiarity with ARIMA modeling, are typically required, along with experience using data visualization tools. Strong analytical thinking, attention to detail, and clear communication skills help in interpreting model results and presenting findings to stakeholders. These skills ensure accurate forecasting, actionable insights, and effective decision-making based on data-driven analysis.
Infographic showing various Forecasting Python Arima Time Series job openings in the United States as of September 2026, with employment types broken down into 3% Internship, 87% Full Time, 6% Part Time, and 4% Contract. Highlights an 73% Physical, 5% Hybrid, and 22% Remote job distribution, with an average salary of $121,932 per year, or $58.6 per hour.

Power Delivery Engineering Co-Op - Load Forecasting & Analytics

Tucker, GA

Georgia System Operations Corporation
Utilities • 201 - 500 employees

Full-time

Posted 23 days ago


Job description

Power Delivery Engineering Co-Op

Load Forecasting & Analytics

Support Grid Reliability Through Forecasting, Data Science, and Advanced Analytics

Georgia System Operations Corporation (GSOC) is seeking a Load Forecasting & Analytics Co-Op to support Power Delivery Engineering. Load and solar forecasts are critical to GSOC operations, and advances in artificial intelligence, machine learning, and computing are creating new opportunities to expand internal forecasting capabilities.

As a Load Forecasting & Analytics Co-Op, you will work alongside engineering and analytics professionals to develop data workflows, support time-series forecasting, maintain vendor application interfaces, and improve the analytic processes used to forecast electric load and solar generation. Your work will help GSOC evaluate new approaches and strengthen the data foundation behind forecasting decisions.

This opportunity is ideal for an analytical and technically curious student who enjoys working with Python, data, predictive models, and complex business problems and wants to apply modern analytics to meaningful challenges in the electric utility industry.

What You'll Do

As a Load Forecasting & Analytics Co-Op, you will support projects and recurring activities that contribute to forecasting accuracy, data quality, analytic capability, and continuous improvement.

You will:

  • Develop andmaintain data workflows that support load and solar forecasting.
  • Collect, integrate, clean, and organize weather data, meter data, and other features used in time-series prediction.
  • Assist with the development, evaluation, and refinement of machine learning and ensemble forecasting models.
  • Support model testing, validation, performance monitoring, and documentation.
  • Maintain and improve application programming interface (API) integrations with vendor forecasting applications.
  • Develop repeatable analytic processes, reports, and tools that improve forecasting workflows and decision support.
  • Use Python, Databricks, Excel, VBA, and other applicable tools to analyze data and automate recurring activities.
  • Research emerging forecasting technologies and methods and summarize potential applications for team review.
  • Present a summary of your projects, findings, recommendations, and key learnings at the conclusion of the assignment.

What You Bring

  • Current enrollment in a bachelor's degree program in Engineering, Data Analytics, or a closely related quantitative field.
  • Interest in load forecasting, solar forecasting, energy analytics, machine learning, predictive modeling, or time-series analysis.
  • Experience using Python for data analysis, modeling, automation, or academic projects.
  • Strong analytical and problem-solving skills, including the ability to organize data,identify patterns, and evaluate results.
  • Attention to detail anda commitment to data quality, model documentation, and reproducible work.
  • Clear written and verbal communication skills, including the ability to explain technical findings to professional audiences.
  • Ability to work independently while collaborating effectively with engineering, analytics, and business partners.
  • Strong organization and time-management skills, including the ability to manage assignments, meet deadlines, and communicate progress.
  • A curious, accountable, and learning-oriented approach to unfamiliar data, tools, and forecasting challenges.

Preferred Qualifications

Forecasting, Machine Learning & Analytics

  • Coursework, research, or project experience involving machine learning, time-series forecasting, predictive analytics, ensemble models, statistics, or data science.
  • Experience preparing features from weather, meter, energy, or other time-series datasets.
  • Interest in developing and comparing forecasting approaches to improve accuracy and business usefulness.

Data Engineering & Technology

  • Experience with Python, Databricks, Excel, or VisualBasic for Applications (VBA).
  • Exposure to APIs, data integration, data workflows, automation, or vendor application interfaces.
  • Ability to organize technical work, document methods, and produce repeatable analytic processes.

Program Information

  • Program Type: Co-Op.
  • Expected Year: Rotation in 2027.
  • Department: Power Delivery Engineering.
  • Location: Tucker, Georgia.
  • Work Arrangement: On-site.

Advantages of Working at GSOC

Working at GSOC means more than completing a co-op assignment--it's an opportunity to contribute to work that matters while developing technical and professional skills that can support your future career.

Meaningful Work

Contribute to forecasting and analytics initiatives that support electric system planning, operational decision-making, and reliable service.

Advanced Analytics Exposure

Gain practical experience with Python, machine learning, ensemble models, time-series data, Databricks, APIs, Excel, VBA, and forecasting workflows.

Coaching & Mentorship

Learn from experienced engineering and analytics professionals who can provide guidance, feedback, and insight into forecasting and electric utility operations.

Professional Development

Build transferable skills through data engineering, modeling, analysis, documentation, technical presentations, feedback, and cross-functional collaboration.

Future Career Opportunities

Successful students may be considered for future co-op rotations, internships, or full-time opportunities when business need, role availability, graduation timing, and qualifications align.

Work Environment

GSOC offers a professional, collaborative learning environment where students work alongside experienced professionals, contribute to meaningful projects, and gain exposure to engineering, analytics, forecasting, and the electric utility industry. Students are expected to follow workplace, safety, confidentiality, data-security, and department-specific requirements while participating in onboarding, regular check-ins, feedback discussions, and end-of-assignment activities.

Program participation does not guarantee future employment. Future opportunities are evaluated according to business need, available positions, graduation timing, and individual qualifications.

Why Join GSOC

At GSOC, your work can contribute to something bigger. You'll gain exposure to the forecasting, data, and analytic processes that help support reliable electric system planning and operations.

You'll also have the opportunity to learn from experienced professionals, contribute to meaningful forecasting initiatives, and explore how your engineering or data analytics background can translate into a future career in the electric utility industry.

Apply Today

If you're ready to apply your analytical skills, technical curiosity, and interest in forecasting and machine learning to meaningful work, we encourage you to apply for the Load Forecasting & Analytics Co-Op opportunity with Power Delivery Engineering.

GSOC does not accept unsolicited resumes or candidate submissions from staffing agencies, search firms, or third-party recruiters. Any unsolicited resumes submitted without a valid, executed agreement will become the property of GSOC, and no placement fee will be paid.


Georgia System Operations Corporation is an Equal Employment Opportunity Employer, including veterans and disabled. We are a drug-free workplace. All applicants are subject to substance abuse testing.