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Time Series Analysis Jobs in California (NOW HIRING)

Ceribell, Inc

Senior/Staff Data Scientist

Sunnyvale, CA · On-site

$184K - $200K/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

Senior/Staff Data Scientist

San Jose, CA

$184K - $200K/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 ...

PIMCO

Data Strategy Lead

Newport Beach, CA · On-site

Your cross-team leadership will drive the development of scalable, reusable analytical frameworks - especially for financial time series analysis, while ensuring rigorous data quality and production ...

PIMCO

Data Strategy Lead

Newport Beach, CA · On-site

Your cross-team leadership will drive the development of scalable, reusable analytical frameworks - especially for financial time series analysis, while ensuring rigorous data quality and production ...

CosQube INC

ML/AI Engineers

Santa Clara, CA · On-site

AIOps platforms, time-series analysis, observability systems * Previous Experience: HAL projects or similar ML platforms strongly preferred * Infrastructure: Kubernetes, ML model deployment and ...

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Time Series Analysis information

See California salary details

$39.5K

$90.8K

$140.6K

How much do time series analysis jobs pay per year?

As of Aug 27, 2026, the average yearly pay for time series analysis in California is $90,761.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,200.00 and $113,500.00 per year, depending on experience, location, and employer.

What is a time series analysis?

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 time series analysis?

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.

What jobs use time series analysis?

Time series analysis is used in various roles such as data analysts, financial analysts, economists, and data scientists. These jobs involve analyzing sequential data to forecast trends, detect anomalies, or inform decision-making, often using tools like Python, R, or specialized software. Professionals in these roles typically require strong statistical skills and experience with data visualization and modeling.

What are the most commonly searched types of Time Series Analysis jobs in California?

The most popular types of Time Series Analysis jobs in California are:

What are popular job titles related to Time Series Analysis jobs in California?

For Time Series Analysis jobs in California, the most frequently searched job titles are:

What job categories do people searching Time Series Analysis jobs in California look for?

The top searched job categories for Time Series Analysis jobs in California are:

What cities in California are hiring for Time Series Analysis jobs?

Cities in California with the most Time Series Analysis job openings:

Time Series Analysis jobs near you
Infographic showing various Time Series Analysis job openings in California as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 100% In-person job distribution, with an average salary of $90,761 per year, or $43.6 per hour.

AI Engineer, Time-Series Signal Processing

BrightAI Corporation

Palo Alto, CA • On-site

Full-time

Re-posted 27 days ago

Job description

AI Engineer, Time-Series Signal Processing
BrightAI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our platform processes visual, spatial, and temporal data from billions of real-world events-captured through edge devices, mobile sensors, and large-scale cloud infrastructure-to deliver intelligent, real-time decisions.
We are now hiring an AI Engineer - Time-Series Signal Processing to lead the development of AI/ML solutions built on high-frequency multi-modal sensor data. This is a critical role focused on modeling and understanding time-series signals coming from IoT devices equipped with various sensors (IMU, acoustic, pressure, temperature, etc.) that drive intelligent automation across physical infrastructure systems.
You'll work on building cutting-edge real-time AI models that process noisy, high-throughput data streams and extract meaningful insights for real-world decision-making-at both the edge and cloud scale.
Responsibilities
  • Design and implement real-time signal processing and ML pipelines for multi-modal time-series data such as those acquired from IMUs, microphones, pressure or force sensors, ultrasonic transducers, and similar sensor sources.
  • Develop and deploy ML models for time-series classification, prediction, anomaly detection, activity recognition, condition monitoring and pattern analysis.
  • Lead research and implementation of RNN-based architectures (especially LSTMs and their variants) as well as temporal transformer models as needed.
  • Build and tune classical and tree-based ML models (XGBoost, LightGBM, Random Forests, and other gradient-boosted ensembles) for time-series tasks, including feature engineering and model interpretability (e.g., SHAP).
  • Work with SCADA systems and industrial telemetry data-ingesting and modeling high-frequency, multi-channel operational data streams from physical assets.
  • Collaborate with hardware, embedded, and product teams to integrate models into edge devices and IoT platforms.
  • Drive experimentation and optimization of signal-processing techniques (e.g., filtering, feature extraction, event detection) to enhance model input quality.
  • Design and maintain scalable workflows for ingesting, labeling, training, and evaluating multi-channel time-series datasets.
  • Stay current with advances in time-series modeling, signal processing, and real-time inference, and incorporate them into product roadmaps.
  • Ensure model robustness, performance, and reliability in production environments, including edge deployments.

Educational Background
  • Degree in Electrical Engineering, Computer Science, or a related field, with a strong focus on signal processing, time-series analysis, and machine learning.
  • Strong academic or industry track record in time-series modeling, signal processing, or real-time AI systems.

Required Skills & Expertise
  • 2+ years of experience developing signal processing and ML solutions for time-series sensor data. Track record of bringing at least one ML solution to market.
  • Deep understanding of digital signal processing (DSP) methods: filtering, sampling, windowing, FFT, feature extraction, etc.
  • Hands-on experience with RNNs (especially LSTMs/GRUs) and/or temporal convolutional networks for time-series modeling.
  • Proficiency with tree-based and gradient-boosting models (XGBoost, LightGBM, Random Forests) applied to time-series and sensor data, including hyperparameter tuning and explainability.
  • Experience working with SCADA systems and industrial telemetry data (high-frequency sensor feeds, time-stamped operational data, multi-channel ingestion from physical assets).
  • Proven experience with time-series data from physical sensors such as IMUs, microphones, vibration or pressure sensors.
  • Strong coding skills in Python and fluency with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras).
  • Experience in optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems.
  • Fluency with best practices in data labeling, augmentation, and evaluation for time-series tasks.
  • Excellent problem-solving and collaboration skills with the ability to work across teams.
  • Strong communication skills with the ability to convey findings and recommendations to internal and external stakeholders.

Bonus Qualifications
  • Experience building end-to-end AI systems for structural health monitoring, condition monitoring, anomaly detection, activity recognition, or motion tracking.
  • Experience with predictive maintenance on industrial equipment using SCADA/telemetry data.
  • Familiarity with experiment tracking and model lifecycle tooling (e.g., MLflow, DVC).
  • Exposure to streaming/online inference patterns (e.g., EWMA normalization, windowed feature extraction on live data).
  • Proficiency in embedded software or deploying models to constrained environments (e.g., using TFLite, ONNX, or custom firmware).
  • Familiarity with containerized workflows and Linux-based development environments.
  • Experience with Agile workflows and tools such as JIRA, Git, and CI/CD pipelines.
  • Prior work in startup or high-pace teams with experience in building real-time systems from the ground up.
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About BrightAI

Sourced by ZipRecruiter

Industry

Software development

Company size

11 - 50 Employees

Headquarters location

San Francisco, CA, US

Year founded

2019

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