We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in ...
We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in ...
You bring deep expertise in ML Ops and time-series forecasting to architect and implement scalable solutions on AWS. Key Responsibilities * Design, develop, and deploy machine learning models for ...
You bring deep expertise in ML Ops and time-series forecasting to architect and implement scalable solutions on AWS. Key Responsibilities * Design, develop, and deploy machine learning models for ...
You bring deep expertise in ML Ops and time-series forecasting to architect and implement scalable solutions on AWS. Key Responsibilities * Design, develop, and deploy machine learning models for ...
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You bring deep expertise in ML Ops and time-series forecasting to architect and implement scalable solutions on AWS. Key Responsibilities * Design, develop, and deploy machine learning models for ...
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.
Data Scientist
Garland, 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
Garland, 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
Wilmer, 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
Wilmer, 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.
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.
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.
Data Scientist
Carrollton, 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
Carrollton, 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
Farmers Branch, 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
Farmers Branch, 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
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.
Data Scientist
Cockrell Hill, 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
Cockrell Hill, 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.
Time Series Forecasting information
See Texas salary details
$48K - $51.9K
22% of jobs
$53.3K is the 25th percentile. Wages below this are outliers.
$51.9K - $55.9K
8% of jobs
$55.9K - $59.8K
19% of jobs
The median wage is $59.9K / yr.
$59.8K - $63.7K
14% of jobs
$67.1K is the 75th percentile. Wages above this are outliers.
$63.7K - $67.7K
14% of jobs
$67.7K - $71.6K
8% of jobs
$71.6K - $75.5K
2% of jobs
$75.5K - $79.5K
1% of jobs
$79.5K - $83.4K
0% of jobs
$83.4K - $87.4K
0% of jobs
$87.4K - $91.3K
12% of jobs
$48K
$64.9K
$91.3K
How much do time series forecasting jobs pay per year?
As of Aug 20, 2026, the average yearly pay for time series forecasting in Texas is $64,903.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,900.00 and $69,400.00 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.
What are the most commonly searched types of Time Series Forecasting jobs in Texas?
The most popular types of Time Series Forecasting jobs in Texas are:
What are popular job titles related to Time Series Forecasting jobs in Texas?
For Time Series Forecasting jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Time Series Forecasting jobs in Texas look for?
The top searched job categories for Time Series Forecasting jobs in Texas are:
Data Science Machine Learning Internship (Summer 2027)
Houston, TX • On-site
Full-time
Re-posted 7 hours ago
Job description
Application Deadline: September 1, 11:59pm EST
Program Summary - Commercial Technology Internships
Company Overview:
Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.
Position Overview:
CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. Our Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.
Responsibilities:
- Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions.
- Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches.
- Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams.
- Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis.
- Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights.
- Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.
Qualifications:
- Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning.
- Expected graduation date of Winter 2027 or Spring/Summer 2028.
- Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics.
- Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
- Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds.
- Strong analytical skills with demonstrated attention to detail.
About Castleton Commodities International
Sourced by ZipRecruiter
Industry
Oil and gas extraction
Company size
501 - 1,000 Employees
Headquarters location
Stamford, CT, US
Year founded
1997