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.
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
Develop and maintain time series forecasting models, incorporating machine learning algorithms and predictive analytics methodologies * Conduct in-depth data analysis and visualization of historical ...
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Mgr - Demand Forecasting and Demand Assurance
Morristown, NJ · On-site
$134K - $148K/yr
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.
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.
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.
Time Series Forecasting information
See salary details
$51.5K - $55.7K
22% of jobs
$57.2K is the 25th percentile. Wages below this are outliers.
$55.7K - $60K
8% of jobs
$60K - $64.2K
19% of jobs
The median wage is $64.3K / yr.
$64.2K - $68.4K
14% of jobs
$72.1K is the 75th percentile. Wages above this are outliers.
$68.4K - $72.6K
14% of jobs
$72.6K - $76.9K
8% of jobs
$76.9K - $81.1K
2% of jobs
$81.1K - $85.3K
1% of jobs
$85.3K - $89.5K
0% of jobs
$89.5K - $93.8K
0% of jobs
$93.8K - $98K
12% of jobs
$51.5K
$69.7K
$98K
How much do time series forecasting jobs pay per year?
As of Aug 20, 2026, the average yearly pay for time series forecasting in the United States is $69,664.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,000.00 and $74,500.00 per year, depending on experience, location, and employer.
What is a time series forecasting?
A Time Series Forecasting job involves analyzing sequential data points collected over time to identify patterns and trends, then using statistical and machine learning models to make future predictions. Professionals in this field work with historical data, applying techniques such as ARIMA, exponential smoothing, and deep learning models like LSTMs. These forecasts help businesses optimize decision-making in areas like sales, finance, inventory management, and demand planning. Strong skills in data analysis, programming (Python, R), and domain expertise are typically required.
What are the key skills and qualifications needed to thrive in time series forecasting?
Excelling in Time Series Forecasting requires a strong background in statistics, mathematics, data analysis, and experience with forecasting methodologies, often supported by a degree in a quantitative field. Proficiency with programming languages such as Python or R, statistical software (e.g., SAS, MATLAB), and familiarity with machine learning frameworks are commonly expected, along with relevant certifications being a plus. Attention to detail, problem-solving skills, and effective communication are important soft skills for interpreting results and collaborating with stakeholders. Mastery of these skills ensures accurate forecasting, actionable insights, and valuable contributions to data-driven business decisions.
What are some common challenges professionals face in time series forecasting?
Professionals in Time Series Forecasting frequently encounter challenges such as handling missing or irregular data, selecting appropriate models for complex real-world scenarios, and accounting for seasonality and trends in datasets. They are often tasked with transforming raw data into a usable format, validating model performance, and continuously refining models as new data becomes available. Collaboration with business teams is essential to ensure forecasts align with organizational goals and are clearly communicated to non-technical stakeholders. Overcoming these challenges requires both technical expertise and effective problem-solving approaches, making the work dynamic and impactful.
More about Time Series Forecasting jobs
What cities are hiring for Time Series Forecasting jobs?
Cities with the most 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 Time Series Forecasting jobs?
States with the most job openings for Time Series Forecasting jobs include:
What job categories do people searching Time Series Forecasting jobs look for?
The top searched job categories for Time Series Forecasting jobs are:
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