Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Data Scientist
Desoto, 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.
Data Scientist
Desoto, 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.
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.
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.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Data Scientist
Highlands, 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.
Data Scientist
Highlands, 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.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Data Scientist
Duncanville, 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.
Data Scientist
Duncanville, 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.
Data Scientist
Dallas, 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.
Data Scientist
Dallas, 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.
Data Scientist
University Park, 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.
Data Scientist
University Park, 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.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Data Scientist
Desoto, 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.
Data Scientist
Desoto, 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.
Mgr - Demand Forecasting and Demand Assurance
Morristown, NJ · On-site
$134K - $148K/yr
Medical
Dental
Vision
Life
Retirement
PTO
Develop and maintain time series forecasting models, incorporating machine learning algorithms and predictive analytics methodologies * Conduct in-depth data analysis and visualization of historical ...
Quick apply
Mgr - Demand Forecasting and Demand Assurance
Morristown, NJ · On-site
$134K - $148K/yr
Medical
Dental
Vision
Life
Retirement
PTO
Develop and maintain time series forecasting models, incorporating machine learning algorithms and predictive analytics methodologies * Conduct in-depth data analysis and visualization of historical ...
Lead development of time series forecasting models (ARIMA, VAR, state-space models, etc.) for business-critical use cases. * Apply econometric techniques such as WLS, panel data models, and causal ...
Lead development of time series forecasting models (ARIMA, VAR, state-space models, etc.) for business-critical use cases. * Apply econometric techniques such as WLS, panel data models, and causal ...
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Data Scientist
Hutchins, 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.
Data Scientist
Hutchins, 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.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
Internship Time Series Forecasting information
See salary details
$11.54 - $12.89
1% of jobs
$12.89 - $14.25
6% of jobs
$14.25 - $15.60
8% of jobs
$16.79 is the 25th percentile. Wages below this are outliers.
$15.60 - $16.96
11% of jobs
$16.96 - $18.31
13% of jobs
The median wage is $19.33 / hr.
$18.31 - $19.67
15% of jobs
$19.67 - $21.02
9% of jobs
$21.98 is the 75th percentile. Wages above this are outliers.
$21.02 - $22.38
17% of jobs
$22.38 - $23.73
16% of jobs
$23.73 - $25.09
2% of jobs
$25.09 - $26.44
2% of jobs
$11
$19
$26
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?
| Aspect | Internship Time Series Forecasting | Data Analyst |
|---|---|---|
| Required Credentials | Basic knowledge of statistics, programming, and time series concepts | Bachelor's degree in data-related fields, some roles may require certifications |
| Work Environment | Internship setting, often in finance, retail, or tech companies | Full-time or part-time roles in various industries, including finance, healthcare, and marketing |
| Employer & Industry Usage | Used for entry-level training and project support in forecasting tasks | Used 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.
More about Internship Time Series Forecasting jobs
What cities are hiring for Internship Time Series Forecasting jobs?
Cities with the most Internship Time Series Forecasting job openings:
What are the most commonly searched types of Time Series Forecasting jobs?
The most popular types of Time Series Forecasting jobs are:
What states have the most Internship Time Series Forecasting jobs?
States with the most job openings for Internship Time Series Forecasting jobs include:
What job categories do people searching Internship Time Series Forecasting jobs look for?
The top searched job categories for Internship Time Series Forecasting jobs are:
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Job description
Required Skills
- 10+ years of hands-on experience in Applied Data Science, Analytics Engineering, and Systems Modeling.
- 5+ years of client-facing, consulting, or business development experience delivering analytics solutions.
- Expertise in statistical modeling, machine learning, and predictive analytics.
- Strong proficiency with Python, scikit-learn, statsmodels, PyTorch, and TensorFlow.
- Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques.
- Strong expertise in geospatial analytics and LiDAR data processing.
- Hands-on experience with ArcGIS, PostGIS, GeoPandas, GDAL/OGR, PDAL, and related geospatial libraries.
- Experience working with vector, raster, point-cloud, and sensor datasets.
- Excellent analytical, communication, and stakeholder management skills.
- Design and develop advanced machine learning and statistical models to solve complex business problems.
- Build predictive models, time-series forecasting solutions, and causal inference frameworks.
- Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets.
- Develop geospatial analytics and LiDAR processing solutions using industry-standard tools and libraries.
- Analyze vector, raster, point-cloud, and sensor data to generate actionable insights.
- Partner with business stakeholders to scope, design, and deliver data science solutions.
- Present analytical findings and recommendations to technical and business audiences.
- Optimize model performance, scalability, and deployment in production environments.
- Mentor data scientists and promote best practices in analytics and machine learning.
- Support innovation initiatives through advanced analytics and AI-driven solutions.
About Techvilla Solutions
Sourced by ZipRecruiter
Industry
It services
Company size
51 - 200 Employees
Headquarters location
CA, US
Year founded
2006