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 ...

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 ...

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 ...

Amazon

Data Scientist, WW Ops FP&A

Bellevue, WA · On-site

... learning, time series analysis, econometrics) to solve complex forecasting challenges Design and implement scalable, reliable approaches to extract insights from large, complex datasets across ...

Amazon

Data Scientist, WW Ops FP&A

Bellevue, WA

... learning, time series analysis, econometrics) to solve complex forecasting challenges Design and implement scalable, reliable approaches to extract insights from large, complex datasets across ...

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 8, 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 87% Full Time, 11% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $80,350 per year, or $38.6 per hour.

Senior Manager - Data Science

Inizio Partners

Philadelphia, PA • Hybrid

$150K - $165K/yr

Full-time

Re-posted 29 days ago

Job description

Role: Senior Manager Data Science

Location: Philadelphia, PA

Type: Hybrid (2-3 days / week in office)

Role Overview

We are looking for a Senior Manager – Data Science (Econometrics & Time Series) to lead advanced analytical initiatives for a major Telecommunications client.

This role is heavily focused on econometric modeling, time series analysis, and causal inference, with applications in forecasting, pricing, and customer behavior analytics. The ideal candidate brings deep expertise in statistical modeling and is comfortable working with large-scale data environments.

Key Responsibilities

  • Lead development of time series forecasting models (ARIMA, VAR, state-space models, etc.) for business-critical use cases.
  • Apply econometric techniques such as WLS, panel data models, and causal inference methods to solve real-world business problems.
  • Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and decision-making.
  • Utilize Markov chains and stochastic processes for modeling sequential or behavioral data.
  • Translate business problems into robust analytical frameworks and deliver actionable insights.
  • Work with large datasets using Databricks
  • Collaborate with stakeholders across business and technical teams to ensure model relevance and impact.
  • Mentor junior team members and drive best practices in statistical modeling and experimentation.

Must-Have Qualifications

  • Strong foundation in econometrics and time series analysis (this is critical for the role).
  • Hands-on experience with:
  • Time series models (ARIMA, SARIMA, VAR, forecasting techniques)
  • Econometric methods (WLS, regression diagnostics, panel data models)
  • Causal inference (A/B testing, quasi-experimental methods)
  • Bayesian statistics and probabilistic modeling
  • Markov chains or stochastic modeling
  • Proficiency in Python along with SQL.
  • Experience working with Databricks or similar big data platforms.
  • Ability to clearly communicate complex statistical concepts to non-technical stakeholders.

Secondary / Good-to-Have Skills (General Data Science)

  • Experience with machine learning models (classification, regression, tree-based models, etc.)
  • Familiarity with feature engineering, model validation, and performance tuning
  • Exposure to ML pipelines and MLOps concepts