Data Scientist Forecasting Seasonal information
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$66.4K - $80.9K
6% of jobs
$80.9K - $95.3K
9% of jobs
$100K is the 25th percentile. Wages below this are outliers.
$95.3K - $109.8K
15% of jobs
The median wage is $119.4K / yr.
$109.8K - $124.2K
22% of jobs
$132.2K is the 75th percentile. Wages above this are outliers.
$124.2K - $138.7K
32% of jobs
$138.7K - $153.1K
3% of jobs
$153.1K - $167.6K
4% of jobs
$167.6K - $182K
1% of jobs
$182K - $196.5K
2% of jobs
How much do data scientist forecasting seasonal jobs pay per year?
As of Aug 16, 2026, the average yearly pay for data scientist forecasting seasonal in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.
Data Scientist Forecasting Seasonal jobs involve analyzing historical data to predict trends and patterns that occur at regular intervals, such as seasons or holidays. These professionals use statistical models, machine learning algorithms, and time series analysis to forecast demand, sales, or other key business metrics. Their insights help organizations plan inventory, staffing, marketing campaigns, and more, especially during periods with predictable fluctuations. Seasonal data scientists often work in industries like retail, agriculture, and tourism, where anticipating seasonal changes is crucial for success.
To thrive as a Data Scientist specializing in Forecasting Seasonal trends, you need a strong background in statistics, time series analysis, and experience with programming languages like Python or R, often supported by a degree in data science, statistics, or a related field. Familiarity with forecasting tools and libraries such as Prophet, ARIMA, or scikit-learn, and proficiency in data visualization platforms like Tableau or Power BI, are typically required. Analytical thinking, attention to detail, and effective communication skills help translate complex data insights into actionable business strategies. These competencies are essential for accurately predicting seasonal patterns, driving informed decision-making, and delivering business value.
Data scientists working on forecasting seasonal trends often encounter challenges such as handling irregular seasonality, managing missing or incomplete data, and accounting for sudden market changes. Overcoming these issues typically involves selecting the right time series models, such as SARIMA or Prophet, that explicitly handle seasonality. Collaborating closely with domain experts and cross-functional teams helps contextualize data anomalies and validate model outputs. Additionally, maintaining robust data pipelines and regularly updating models ensures forecasts remain accurate as new patterns emerge.
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