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.
New
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
New
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.
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.
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 ...
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.
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.
New
Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques. * Strong expertise in geospatial analytics and LiDAR data processing.
New
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.
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.
Applied statistics, time-series forecasting (ARIMA, Prophet, neural methods) * Retail demand planning or demand sensing experience * SageMaker model training and deployment * Python, SQL, experience ...
Applied statistics, time-series forecasting (ARIMA, Prophet, neural methods) * Retail demand planning or demand sensing experience * SageMaker model training and deployment * Python, SQL, experience ...
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.
Evening Time Series Forecasting information
See salary details
$17.07 - $19.60
4% of jobs
$19.60 - $22.14
14% of jobs
$23.11 is the 25th percentile. Wages below this are outliers.
$22.14 - $24.67
18% of jobs
$24.67 - $27.21
13% of jobs
The median wage is $27.42 / hr.
$27.21 - $29.74
13% of jobs
$29.74 - $32.28
7% of jobs
$33.83 is the 75th percentile. Wages above this are outliers.
$32.28 - $34.81
10% of jobs
$34.81 - $37.35
6% of jobs
$37.35 - $39.88
7% of jobs
$39.88 - $42.42
3% of jobs
$42.42 - $44.95
4% of jobs
$17
$29
$44
How much do evening time series forecasting jobs pay per hour?
As of Aug 17, 2026, the average hourly pay for evening time series forecasting in the United States is $29.83, according to ZipRecruiter salary data. Most workers in this role earn between $23.56 and $34.62 per hour, depending on experience, location, and employer.
What is the difference between Evening Time Series Forecasting vs Data Analyst?
| Aspect | Evening Time Series Forecasting | Data Analyst |
|---|---|---|
| Credentials | Degree in statistics, data science, or related field; experience with forecasting models | Degree in statistics, data analysis, or related field; proficiency in data tools |
| Work Environment | Typically in finance, retail, or logistics sectors; focus on forecasting during evening hours | Varies across industries; analyzing data to inform business decisions |
| Industry Usage | Used for demand planning, sales forecasting, inventory management | Used for reporting, data interpretation, and strategic insights |
Evening Time Series Forecasting specializes in predicting future data trends during evening hours, often focusing on demand and sales patterns. Data Analysts interpret data to support decision-making across various industries. While both roles require analytical skills and familiarity with data tools, Forecasting roles emphasize modeling and prediction, whereas Data Analysts focus on data interpretation and reporting.
More about Evening Time Series Forecasting jobs
What cities are hiring for Evening Time Series Forecasting jobs?
Cities with the most Evening 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 Evening Time Series Forecasting jobs?
States with the most job openings for Evening Time Series Forecasting jobs include:
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