ZipRecruiter

Log In

2

Entry Level Time Series Analysis Jobs (NOW HIRING)

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 ...

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 ...

Hp

Print Supplies Big Data Analyst

Vancouver, WA · On-site

$105K - $161K/yr

... time-series analysis, reproducible workflows, and tooling to support recurring quality checks and investigations SQL , including complex joins, CTEs, window functions, query performance optimization ...

next page

Showing results 1-20

All JobsTime Series Analysis JobsEntry Level Time Series Analysis Jobs

Entry Level Time Series Analysis information

See salary details

$14

$38

$68

How much do entry level time series analysis jobs pay per hour?

As of Jul 29, 2026, the average hourly pay for entry level time series analysis in the United States is $38.63, according to ZipRecruiter salary data. Most workers in this role earn between $25.96 and $48.32 per hour, depending on experience, location, and employer.

What is an entry level time series analyst?

An entry level time series analyst is a professional who assists in collecting, processing, and analyzing data that is sequenced over time, such as sales trends, stock prices, or weather patterns. Typically, they use statistical techniques and software tools to identify patterns, make forecasts, and support business or research decisions. Entry level analysts often work under the supervision of senior analysts or data scientists and may be responsible for tasks like data cleaning, visualization, and running basic models. This role is suitable for recent graduates with a background in statistics, mathematics, economics, or related fields, and some familiarity with programming or analytics software.

Is 30 too late for data science?

Entry level time series analysis roles in data science do not have an age limit; many professionals transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and tools like Python or R, regardless of age. Continuous learning and building a strong portfolio can help late entrants enter the field effectively.

What are some typical challenges faced by entry-level professionals in time series analysis, and how can they overcome them?

Entry-level time series analysts often encounter challenges such as managing large and complex datasets, selecting appropriate models, and interpreting results accurately. Learning to preprocess data (e.g., handling missing values or outliers) and understanding the assumptions behind common models like ARIMA or exponential smoothing are essential. Collaborating closely with senior analysts and data scientists can provide practical guidance and feedback, while ongoing training in statistical software (such as Python or R) helps build confidence. Over time, developing a systematic approach to model selection and validation will improve both accuracy and efficiency.

What are the key skills and qualifications needed to thrive as an Entry Level Time Series Analyst, and why are they important?

To thrive as an Entry Level Time Series Analyst, a solid background in statistics, mathematics, and data analysis—often demonstrated through a relevant degree—is essential. Familiarity with statistical software such as R or Python (with libraries like pandas and statsmodels), and experience using data visualization tools are typically expected. Strong attention to detail, critical thinking, and effective communication skills help in accurately interpreting data trends and presenting findings to non-technical stakeholders. These skills and qualities are crucial for producing reliable analyses that support informed decision-making in business and research environments.

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

AspectEntry Level Time Series AnalysisData Analyst
Required CredentialsBachelor's in Statistics, Data Science, or related field; basic knowledge of time series methodsBachelor's in Statistics, Data Science, or related field; proficiency in data manipulation and visualization
Work EnvironmentFinancial firms, tech companies, or research institutions focusing on forecasting and trend analysisVarious industries including marketing, finance, healthcare, analyzing datasets to inform business decisions
Common UsageAnalyzing time-dependent data, forecasting, identifying seasonal patternsInterpreting data, creating reports, supporting decision-making across departments

While both roles require a strong foundation in data analysis and similar educational backgrounds, Entry Level Time Series Analysis focuses specifically on analyzing and forecasting time-dependent data, often in finance or research settings. Data Analysts have a broader scope, working with various data types to generate insights across multiple industries.

What jobs use time series analysis?

Entry level time series analysis skills are used in roles such as data analyst, financial analyst, and operations analyst, where analyzing sequential data helps in forecasting and decision-making. These jobs often require proficiency with tools like Python, R, or Excel, and involve working with financial, sales, or sensor data to identify trends and patterns.

How to get into data analysis with no experience?

Entry level time series analysis roles typically require foundational skills in statistics, programming (such as Python or R), and data visualization tools. Gaining experience through online courses, internships, or personal projects can help build a portfolio and demonstrate your abilities to employers.

What jobs pay 4000 a week without a degree?

Entry-level roles in time series analysis typically do not pay $4,000 a week without experience or specialized skills. High-paying jobs in data analysis or finance may reach that level, but they usually require advanced skills, certifications, or experience beyond entry level. Most roles paying this amount are in senior positions or require significant expertise and credentials.
What are the most commonly searched types of Time Series Analysis jobs? The most popular types of Time Series Analysis jobs are:
What job categories do people searching Entry Level Time Series Analysis jobs look for? The top searched job categories for Entry Level Time Series Analysis jobs are:
Entry Level Time Series Analysis jobs near you
Infographic showing various Entry Level Time Series Analysis job openings in the United States as of July 2026, with employment types broken down into 85% Full Time, 13% Part Time, and 2% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $80,350 per year, or $38.6 per hour.
AI Engineer, Time-Series Signal Processing

AI Engineer, Time-Series Signal Processing

BrightAI Corporation

Palo Alto, CA • On-site

Full-time

Posted 28 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.
BrightAI logo

About BrightAI

Sourced by ZipRecruiter

Industry

Software development

Company size

11 - 50 Employees

Headquarters location

San Francisco, CA, US

Year founded

2019

View All BrightAI Jobs

Apply