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Freelance Time Series Forecasting Jobs (NOW HIRING)

Inherent Technologies

Data Scientist

Chandler, AZ · Remote

Strong Time Series forecasting, ML, deep learning and standard statistical methods to evaluate models. Experience working on supply chain projects. We are seeking a highly skilled Data Scientist to ...

Amazon

Data Scientist, Demand Forecasting

Bellevue, WA · On-site

At Amazon, our Demand Forecasting team is tackling one of the most ambitious challenges in applied time series research: building large-scale foundation models that generalize across an enormous and ...

Redolent, Inc.

Sr, ML Architect

Washington, DC · On-site

The ideal candidate will have a strong background in time series forecasting, anomaly detection, event classification, and correlation ML algorithms. Additionally, experience in integrating with ...

Redolent, Inc.

AI/ML Engineer

Phoenix, AZ · On-site

This role requires hands-on experience with time series forecasting, anomaly detection, event classification, and correlation ML algorithms. Additionally, experience in integrating with large ...

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Freelance Time Series Forecasting information

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How much do freelance time series forecasting jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for freelance time series forecasting in the United States is $47.71, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $61.78 per hour, depending on experience, location, and employer.

What are some common challenges freelance time series forecasters face when working with clients?

Freelance time series forecasters often encounter challenges such as limited access to high-quality or complete datasets, which can impact the accuracy of their models. Communicating technical findings to clients with varying levels of statistical knowledge can also be demanding, requiring clear and actionable reporting. Additionally, managing multiple projects with different timelines and expectations requires strong organizational skills. Building trust with clients by explaining model limitations and providing transparent forecasts is key to long-term success in this role.

What is the difference between Freelance Time Series Forecasting vs Freelance Data Analyst?

AspectFreelance Time Series ForecastingFreelance Data Analyst
CredentialsStatistical, data analysis, or domain-specific certificationsData analysis, statistics, or related certifications
Work EnvironmentRemote, project-based, often specialized in forecasting modelsRemote or on-site, broader data analysis tasks across industries
Industry UsageFinance, supply chain, sales forecastingMarketing, operations, business intelligence
Search & Comparison IntentFocus on predictive modeling and forecasting skillsBroader data analysis and reporting skills

Freelance Time Series Forecasting specialists focus on creating models to predict future data points, often in finance or supply chain contexts. Freelance Data Analysts handle a wider range of data tasks, including reporting and insights. While both roles require analytical skills, forecasting is more specialized in predictive modeling, whereas data analysis covers broader data interpretation.

What are the key skills and qualifications needed to thrive as a Freelance Time Series Forecaster, and why are they important?

To thrive as a Freelance Time Series Forecaster, you need a strong background in statistics, mathematics, and data analysis, often supported by a relevant degree and experience with forecasting methodologies. Proficiency in statistical programming languages (like Python or R), machine learning libraries (such as scikit-learn, statsmodels, or Prophet), and data visualization tools is typically required. Strong problem-solving skills, clear communication, and the ability to manage client relationships are crucial soft skills. These competencies enable accurate forecasting, effective project delivery, and successful collaboration with clients in data-driven environments.

What is freelance time series forecasting?

Freelance time series forecasting involves independently analyzing and predicting future data points based on historical time-ordered data for clients or organizations. Freelancers in this field typically use statistical and machine learning methods to identify patterns and trends in data such as sales, stock prices, or weather. They may work on short-term projects or ongoing contracts, providing insights that help clients make informed business decisions. The flexibility of freelancing allows experts to work with various industries and datasets, often remotely.
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What are the most commonly searched types of Time Series Forecasting jobs? The most popular types of Time Series Forecasting jobs are:
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Freelance Time Series Forecasting jobs near you
Infographic showing various Freelance Time Series Forecasting job openings in the United States as of July 2026, with employment types broken down into 98% Full Time, 1% Part Time, and 1% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $99,230 per year, or $47.7 per hour.
Adjunct Instructor in Time Series Forecasting and Operational Analytics

Adjunct Instructor in Time Series Forecasting and Operational Analytics

Brandeis University

Waltham, MA • On-site

$6.5K/mo

Part-time

Posted 24 days ago

Job description

Brandeis University's Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 135 Time Series Forecasting and Operational Analytics for the Fall-2 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Applied Data Science and Decision Analytics.
This course will cover predictive modeling and forecasting under uncertainty, including ARIMA, Prophet, and deep learning approaches for sustainable operations.
Core Course Responsibilities Summary
  • Course Logistics and Facilitation: Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms.
  • Instructor Presence and Engagement: Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation.
  • Individual Feedback and Grading: Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe.
  • Professional Conduct and Standards: Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term.

Qualifications:
  • Required:
    • Advanced degree (Masters or Ph.D) in Statistics, Operational Research, Data Science or a related field.
    • Professional experience applying forecasting methods to operational demands, planning, or in sustainability contexts.
    • Expertise in time series analysis and forecasting under uncertainty, including ARMIA and modern machine learning approach.
    • At least 1 year of teaching or training experience (preferably online/asynchronous)
    • Experience with online instruction
    • Excellent communication and teaching skills in an online learning environment.
  • Preferred:
    • Prior online teaching experience at the graduate level
    • Knowledge of global learner personas and culturally responsive pedagogy
    • Familiarity with Moodle LMS and digital authoring tools (e.g., H5P)

Interested candidates should submit:
A cover letter highlighting relevant qualifications and teaching experience.
A current CV or resume.
Contact information for three professional references.
Application review begins June 1, 2026 though we will continue to accept submissions on an ongoing basis.
This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509.
Compensation for this positon is: $6573.15
Pay Range Disclosure
The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements.
Equal Opportunity Statement
Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").

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