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Internship Time Series Analysis Jobs (NOW HIRING)

M&T Bank

Model Risk Analyst-Validation

Buffalo, NY

$91K - $101K/yr

Data Science concepts, including statistics and probability, exploratory data analysis (EDA), machine learning, model evaluation and selection, feature engineering, time series analysis, loss ...

M&T Bank

Model Risk Analyst-Validation

Buffalo, NY · On-site

$91K - $101K/yr

Data Science concepts, including statistics and probability, exploratory data analysis (EDA), machine learning, model evaluation and selection, feature engineering, time series analysis, loss ...

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How much do internship time series analysis jobs pay per hour?

As of Jul 27, 2026, the average hourly pay for internship time series analysis in the United States is $15.54, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $17.55 per hour, depending on experience, location, and employer.

What types of projects or tasks can I expect to work on during an internship in time series analysis?

As an intern specializing in time series analysis, you will typically assist with collecting, cleaning, and analyzing temporal data using statistical software such as Python or R. Your projects may include forecasting trends, detecting anomalies, and visualizing patterns in data sets from fields like finance, healthcare, or operations. You will often collaborate with data scientists, analysts, and business stakeholders to translate findings into actionable insights, while learning to apply techniques such as ARIMA, exponential smoothing, or machine learning models. This hands-on experience is a valuable foundation for pursuing advanced roles in data science and analytics.

What is the difference between Internship Time Series Analysis vs Data Analyst?

AspectInternship Time Series AnalysisData Analyst
Required CredentialsRelevant coursework, basic statistical knowledgeBachelor's degree in related field, some certifications
Work EnvironmentInternship setting, supervised projectsFull-time or part-time professional role
Industry UsageEntry-level, learning-focusedBusiness, finance, healthcare, and more

Internship Time Series Analysis is an entry-level, learning-focused role typically performed during internships, emphasizing foundational skills in analyzing time-based data. In contrast, Data Analysts are more experienced professionals responsible for interpreting data to inform business decisions. While both roles involve data analysis, internships are more about gaining experience, whereas Data Analysts perform ongoing, complex analysis in various industries.

What are the key skills and qualifications needed to thrive as an Internship Time Series Analysis, and why are they important?

To thrive as an Internship Time Series Analysis, you need a solid grounding in statistics, data analysis, and proficiency in mathematical concepts, usually supported by coursework in mathematics, statistics, or data science. Familiarity with technical tools such as Python or R, and experience with libraries like pandas, NumPy, and statsmodels, as well as data visualization platforms, are highly valuable. Analytical thinking, problem-solving abilities, and strong communication skills set candidates apart in interpreting results and presenting findings. These skills ensure accurate modeling, effective data-driven insights, and clear communication of complex temporal patterns for impactful business or research decisions.

What is an internship in time series analysis?

An internship in time series analysis is a temporary position, often for students or recent graduates, where you gain hands-on experience analyzing data that is collected over time. Interns typically work with datasets to identify trends, patterns, and make forecasts using statistical and machine learning techniques. The role may involve using tools like Python, R, or specialized software to clean data, build models, and visualize results. It’s a valuable opportunity to apply theoretical knowledge from coursework to real-world problems in industries such as finance, economics, or technology.
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Internship Time Series Analysis jobs near you
Infographic showing various Internship Time Series Analysis job openings in the United States as of July 2026, with employment types broken down into 42% Full Time, 42% Part Time, 8% Temporary, and 8% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $32,333 per year, or $15.5 per hour.
AI Engineer, Time-Series Signal Processing

AI Engineer, Time-Series Signal Processing

BrightAI Corporation

Palo Alto, CA • On-site

Full-time

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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