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Python Data Analyst Jobs in Philadelphia, PA (NOW HIRING)

ASRC Federal Holding

Senior Data Analyst

Moorestown, NJ · On-site

$96K - $164K/yr

We are currently seeking a Senior Data Analyst to join our teamin Moorestown, NJ. Responsibilities ... Hands-on experience with PyTorch and related Python libraries * Experience with unsupervised ...

Vangard, Inc.

Data Analyst, Senior Specialist

Malvern, PA

$84K - $106K/yr

We are seeking an experienced data analyst to support the Flagship Client segment and the ... Demonstrates deep expertise in one or more technical domains (e.g., Python, advanced analytics ...

BNY

Senior Specialist, Data Analyst & BI

Berwyn, PA · On-site

$82K - $104K/yr

Develop and implement data analytics strategies by utilizing advanced analytical tools and ... Developing Oracle SQL, Python, and Perl. * Experience with Bert is a plus. * Excellent ...

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All JobsPython Data Analyst JobsPython Data Analyst Jobs in Philadelphia, PA

Python Data Analyst information

See Philadelphia, PA salary details

$34.3K

$83.4K

$137.2K

How much do python data analyst jobs pay per year?

As of Sep 10, 2026, the average yearly pay for python data analyst in Philadelphia, PA is $83,391.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,100.00 and $97,900.00 per year, depending on experience, location, and employer.

What does a Python data analyst do?

A Python Data Analyst leverages the Python programming language to collect, process, and analyze large sets of data. They use tools and libraries like Pandas, NumPy, and Matplotlib to clean data, perform statistical analysis, and create visualizations that help organizations make data-driven decisions. Their role often involves extracting insights from complex datasets, automating data workflows, and communicating findings to stakeholders through reports or dashboards. Python Data Analysts play a crucial part in turning raw data into actionable business intelligence.

What does a Python data analyst do?

As a Python data analyst, you use the Python programming language to develop tools for data mining, analysis, and data visualization. You typically develop a script to meet the specific data needs of your client or employer. Then, you test your code and perform debugging duties before deploying it in a live environment. Some data analysts also have algorithm creation responsibilities. In this case, after creating and testing an algorithm, you use Python with your algorithm to interpret data. You also develop reports to show to your clients or employers, and you may code a web app or interface that clients can use to visualize data sets.

What are the key skills and qualifications needed to thrive as a Python data analyst, and why are they important?

To thrive as a Python Data Analyst, you need strong analytical skills, a solid grasp of statistics, and proficiency in Python programming, often supported by a degree in data science, mathematics, or a related field. Familiarity with data analysis libraries like pandas and NumPy, visualization tools such as Matplotlib or Seaborn, and experience with data querying languages like SQL are typically required. Attention to detail, critical thinking, and effective communication help you derive insights and present findings clearly to stakeholders. These skills and qualities are vital for transforming raw data into actionable business intelligence and supporting data-driven decision-making.

How do Python data analysts typically collaborate with other departments within an organization?

Python Data Analysts often work closely with teams such as marketing, finance, and product development to provide data-driven insights that inform business decisions. They regularly participate in cross-functional meetings to understand departmental objectives, gather requirements for data analysis, and present their findings in an accessible manner. Effective communication and the ability to translate technical results into actionable recommendations are essential, as analysts often act as a bridge between technical data and non-technical stakeholders.

What is the difference between Python Data Analyst vs Data Scientist?

AspectPython Data AnalystData Scientist
Required SkillsPython, SQL, data visualization, statistical analysisPython, R, machine learning, statistical modeling
Work EnvironmentBusiness analytics, reporting, data cleaningAdvanced modeling, predictive analytics, research
Industry UsageFinance, marketing, healthcare, retailTech, finance, research, AI development

While both roles require Python and data analysis skills, Data Scientists typically engage in more complex modeling and machine learning, whereas Python Data Analysts focus on data cleaning, visualization, and reporting to support business decisions.

Is Python good for data analysts?

Python is widely used by data analysts due to its simplicity, extensive libraries like pandas and NumPy, and strong community support. It enables efficient data manipulation, analysis, and visualization, making it a valuable skill for the role.
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Python Data Analyst jobs near you
Infographic showing various Python Data Analyst job openings in Philadelphia, PA as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $83,391 per year, or $40.1 per hour.

Marketing Data Analyst

Malvern, PA • On-site

Kanak Elite Services Inc
Software Development • 51 - 200 employees
Learn more about Kanak Elite Services Incexternal

Contractor

Re-posted 22 days ago

Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Marketing Data Analyst with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Position           : Marketing Data Analyst

Location          : Malvern, PA Hybrid

Duration         : long term contract

Responsibilities

  • Translate marketing and business needs into analytical requirements E.g., data, models required to build strategic analytical solution
  • Establish the analytical framework to successfully deliver business OKRs
  • Design a robust and streamlined data-lead strategy to drive marketing optimization at scale
  • Run BAU, proposed and other “what if” scenarios assessments to validate approach and evaluate impact
  • Design an actionable marketing operations and experimentation roadmap
  • Build consensus and influence senior stakeholders to gain confidence in analytics proposals
  • Continuously evangelize analytical and data-driven problem solving, strategic mindset & experimentation best practices across the organization

Qualifications

  • Bachelor’s degree and industry certification preferred, 8+ years’ experience and relevant skillset in the subject matter required, has extensive knowledge of subject matter and extensive knowledge of the business

Key Focus Areas:

    • The analyst will work within the Marketing Analytics team, which supports multiple business partners across various marketing functions.
    • This role involves leveraging existing data science models—developed by a separate data science team—to design, execute, and optimize marketing campaigns.
    • The analyst will focus on translating model outputs into actionable marketing strategies, helping the business understand how to use data to improve campaign effectiveness and targeting.
    • A solid understanding of marketing analytics concepts, campaign creation, and performance optimization is essential.
    • Experience with forecasting, data-driven decision-making, and campaign performance measurement will be highly valuable.

Technical Requirements:

    • Proficiency in SQL for data extraction, manipulation, and analysis.
    • Working knowledge of Python for data analysis and automation.
    • Strong analytical and communication skills to collaborate with marketing stakeholders and translate technical insights into business outcomes.
    • While domain experience isn't important these resources need to have supported large enterprises, worked with large sets of data and supported big data platforms (AWS or Azure)