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Time Series Analysis Jobs (NOW HIRING)

Ceribell, Inc

Data Scientist

Sunnyvale, CA · On-site

$184K - $210K/yr

Use time-series analysis, statistical signal processing and machine learning techniques to design classification algorithms for various neurological indications * Analyze data for trends and patterns ...

Ceribell, Inc

Data Scientist

Sunnyvale, CA · On-site

$184K - $210K/yr

Use time-series analysis, statistical signal processing and machine learning techniques to design classification algorithms for various neurological indications * Analyze data for trends and patterns ...

Ceribell, Inc

Data Scientist

Sunnyvale, CA

$184K - $210K/yr

Use time-series analysis, statistical signal processing and machine learning techniques to design classification algorithms for various neurological indications * Analyze data for trends and patterns ...

Info Way Solutions

Timeseries Developer

Fremont, CA · On-site

Implement algorithms for time series analysis, trend detection, and anomaly detection. Implement best practices in software development, including DevOps. Collaborate with cross-functional teams to ...

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How much do time series analysis jobs pay per year?

As of May 28, 2026, the average yearly pay for time series analysis in the United States is $91,965.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $115,000.00 per year, depending on experience, location, and employer.

What is a Time Series Analysis job?

A Time Series Analysis job involves analyzing data points collected or recorded at specific time intervals to identify patterns, trends, and seasonal variations. Professionals in this role use statistical methods, machine learning models, and forecasting techniques to interpret historical data and make future predictions. Common applications include finance, economics, weather forecasting, and business performance analysis. Strong analytical skills, programming knowledge (e.g., Python, R), and experience with time series models like ARIMA or Prophet are typically required.

What are the key skills and qualifications needed to thrive in the Time Series Analysis position, and why are they important?

To excel in Time Series Analysis, you need a strong background in statistics, data analysis, and mathematics, often supported by a degree in statistics, economics, computer science, or a related field. Experience with programming languages such as Python or R, familiarity with statistical packages, and knowledge of specialized tools like ARIMA or machine learning libraries are highly beneficial. Attention to detail, problem-solving skills, and clear communication are important for effectively interpreting data and sharing insights with stakeholders. These competencies enable professionals to extract meaningful trends from complex datasets, inform strategic decisions, and provide valuable forecasts for businesses.

What are some common challenges faced in a Time Series Analysis role and how are they addressed?

Professionals in Time Series Analysis often encounter challenges such as handling missing or noisy data, accounting for seasonality and trends, and selecting the most appropriate modeling techniques for complex real-world problems. Addressing these challenges typically involves thorough data preprocessing, applying advanced statistical or machine learning models, and collaborating with domain experts to understand business contexts. Regularly updating models and validating forecasts also ensures accuracy and relevance. This dynamic environment helps analysts develop their expertise, adopt innovative solutions, and contribute significantly to data-driven decision-making within their organizations.
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Time Series Analysis jobs near you
Infographic showing various Time Series Analysis job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $91,965 per year, or $44.2 per hour.

Senior Data Scientist - Manufacturing Analytics

Macpower Digital Assets Edge

Sunnyvale, CA • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Summary: We are seeking a highly experienced Data Scientist to join our analytics team, with a focus on advanced data modeling and AI applications in manufacturing. This role requires strong expertise in statistical modeling, machine learning, and time series analysis, coupled with a solid foundation in programming and data manipulation.
Key Responsibilities:
  • Data Analysis: Analyze large, complex datasets to uncover actionable insights for business and manufacturing operations.
  • Model Development: Design and implement machine learning models, including predictive and statistical models (e.g., regression, classification, clustering).
  • Time Series Modeling: Build and validate models using time-series data for forecasting and anomaly detection in industrial environments.
  • Visualization: Create dashboards and reports to visualize key metrics using tools such as Matplotlib and Seaborn.
  • Collaboration: Partner with cross-functional teams-engineering, product, marketing-to gather requirements and deliver high-impact data solutions.
  • Data Quality: Ensure data integrity through preprocessing, cleaning, and validation routines.
  • Reporting: Communicate findings to stakeholders through presentations and documentation.

Required Qualifications:
  • Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related discipline.
  • 5+ years of experience in data science or analytics roles.
  • Proficiency in Python or R.
  • Experience with statistical/empirical model building.
  • Strong knowledge of machine learning techniques: regression, classification, clustering, neural networks.
  • Extensive experience with time series data modeling and analysis.
  • Hands-on experience with Python libraries: pandas, NumPy, SciPy.
  • Experience with SQL and relational databases.
  • Familiarity with data visualization tools such as Matplotlib and Seaborn.

Preferred Skills:
  • pplied experience in manufacturing environments or industrial analytics.
  • Experience deploying AI solutions in production systems.
  • Knowledge of big data technologies: Hadoop, Spark.
  • Cloud platform exposure (AWS, Google Cloud, Azure).
  • Familiarity with version control systems like Git.

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