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Assistant Data Analyst Jobs in Maple Shade, NJ (NOW HIRING)

Apply AI coding and analysis assistants to accelerate your own work, while learning to evaluate ... Core Qualifications * 3+ years of data science / ML experience * Bachelor's degree in Statistics ...

Analyze, interpret and enter changes in an accurate and timely manner * Own assigned processes for ... Generate basic data reports as requested and assist in process documentation/updates. Ensure ...

... and maintain financial data integrity. * Assist with system testing, enhancements, and ... Strong analytical, problem solving, attention to detail, and independent decision-making skills.

... and maintain financial data integrity. * Assist with system testing, enhancements, and ... Strong analytical, problem solving, attention to detail, and independent decision-making skills.

... and maintain financial data integrity. * Assist with system testing, enhancements, and ... Strong analytical, problem solving, attention to detail, and independent decision-making skills.

Claude, Copilot, or similar) to assist with data visualization development * 1-3+ years of experience in a technical role within data and analytics * Ability to work with data from multiple systems ...

Claude, Copilot, or similar) to assist with data visualization development * 1-3+ years of experience in a technical role within data and analytics * Ability to work with data from multiple systems ...

Showing results 21-40

Assistant Data Analyst information

See Maple Shade, NJ salary details

$27.9K

$71.3K

$136.4K

How much do assistant data analyst jobs pay per year?

As of Aug 17, 2026, the average yearly pay for assistant data analyst in Maple Shade, NJ is $71,335.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,800.00 and $83,100.00 per year, depending on experience, location, and employer.

What does an assistant data analyst do?

An Assistant Data Analyst supports data collection, cleaning, and analysis tasks to help organizations make informed decisions. They work under the guidance of senior analysts to prepare reports, visualize data trends, and ensure the accuracy of data sets. Their responsibilities may include using tools like Excel, SQL, or Python to process data, as well as communicating findings to different departments. Assistant Data Analysts play a crucial role in ensuring that data-driven projects run smoothly and efficiently.

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

To thrive as an Assistant Data Analyst, you need a solid understanding of statistics, data interpretation, and proficiency in data management, usually backed by a degree in a quantitative field. Familiarity with tools like Microsoft Excel, SQL, and data visualization platforms such as Tableau or Power BI is typically required. Strong attention to detail, problem-solving abilities, and effective communication skills help you stand out in this role. These competencies ensure accurate data analysis, clear reporting, and valuable insights that support informed business decisions.

What are some common challenges an assistant data analyst might face when starting in this role?

Assistant Data Analysts often encounter challenges related to handling large datasets, learning new analytical tools, and ensuring data accuracy. Adjusting to the fast-paced environment where priorities can shift quickly is also common, as is collaborating with various departments to gather relevant data. Over time, gaining familiarity with organizational data sources and building strong communication with senior analysts helps overcome these initial hurdles.

What is the difference between Assistant Data Analyst vs Data Analyst?

AspectAssistant Data AnalystData Analyst
Required CredentialsBachelor's degree in related field, basic knowledge of data toolsBachelor's or higher, proficiency in data analysis software
Work EnvironmentSupportive team, entry-level tasks, supervised rolesIndependent analysis, project ownership, more complex tasks
Employer & Industry UsageEntry-level roles across industries like finance, healthcare, marketingMid-level roles, often with more responsibility and specialization

The main difference between an Assistant Data Analyst and a Data Analyst lies in experience and responsibility. Assistant Data Analysts typically handle basic data tasks under supervision, while Data Analysts perform more complex analysis independently. Both roles require similar educational backgrounds, but Data Analysts usually have more experience and technical skills.

What are the most commonly searched types of Data Analyst jobs in Maple Shade, NJ?

The most popular types of Data Analyst jobs in Maple Shade, NJ are:

What are popular job titles related to Assistant Data Analyst jobs in Maple Shade, NJ?

For Assistant Data Analyst jobs in Maple Shade, NJ, the most frequently searched job titles are:

What job categories do people searching Assistant Data Analyst jobs in Maple Shade, NJ look for?

The top searched job categories for Assistant Data Analyst jobs in Maple Shade, NJ are:

What cities near Maple Shade, NJ are hiring for Assistant Data Analyst jobs?

Cities near Maple Shade, NJ with the most Assistant Data Analyst job openings:

Infographic showing various Assistant Data Analyst job openings in Maple Shade, NJ as of June 2026, with employment types broken down into 98% Full Time, 1% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $71,335 per year, or $34.3 per hour.

Data Analyst, Specialist

Vanguard Group, Inc.

Malvern, PA • On-site

Full-time

Re-posted 13 days ago


Vanguard rating

8.7

Company rating: 8.7 out of 10

Based on 64 frontline employees who took The Breakroom Quiz

16th of 150 rated financial services


Job description

Role Summary
In this role, you will help turn data into decisions by combining strong technical execution with growing business awareness and communication skills. Working alongside more senior scientists and cross-functional partners, you will contribute to solving real business problems and learn how analytics connects to outcomes.
You'll own well-defined components - a model, a feature pipeline, an analysis - while developing the ability to understand stakeholder needs, ask the right questions, and explain your work clearly so others can act on it. The problems will often arrive partially framed; your role is to execute rigorously while building the judgment to connect technical outputs to business value.
This is a hands-on, growth-oriented role on cross-functional teams where you'll build both technical depth and the communication skills needed to become a trusted analytics partner over time.
What You'll Do
Explain your work clearly to technical and non-technical teammates. Communicate methods, results, and limitations so findings are understood, trusted, and usable in decision-making.
Build well-scoped models and analyses. Develop and validate models on defined problems such as feature engineering, model fitting, calibration, and validation with guidance on approach and standards.
Wrangle and prepare data. Access, transform, clean, and document large-scale data; identify and diagnose inconsistencies and gaps.
Contribute to production. Help deploy and monitor models alongside MLE and engineering, learning the discipline of keeping a live model healthy.
Run experiments others design. Execute designed experiments and analyses correctly and interpret the results.
Explain your work clearly. Communicate methods, results, and caveats to your team so findings can be trusted and built on.
Use AI to work faster. Apply AI coding and analysis assistants to accelerate your own work, while learning to evaluate their output critically.
Learn the practice. Absorb standards and patterns from senior teammates and contribute to a growing, AI-native analytics community.
Core Qualifications
  • 3+ years of data science / ML experience

  • Bachelor's degree in Statistics, Applied Mathematics, Computer Science, Economics, Analytics, or a related quantitative field - or an equivalent combination of training and experience. Grad degree preferred.

  • Working proficiency in Python and SQL and comfort wrangling real, messy data.

  • Solid foundation in statistical and machine learning methods and an understanding of model validation.

  • Exposure to cloud environments (AWS, Azure, or GCP) and standard tooling (e.g., Git, Jupyter).

  • Clear communication and a strong desire to learn.

Building for the Age of AI
We expect this role to use modern AI tools fluently and to grow into building with them. Strength or genuine curiosity in several of the following is what we're looking for:
  • Working with GenAI / LLMs: comfort using retrieval-augmented generation (RAG), embeddings, and prompting following established patterns.

  • Building alongside agentic systems: contributing to LLM/agent workflows that someone more senior has architected.

  • Evaluation basics: helping test model and LLM output against defined quality metrics.

  • Experimentation fundamentals: understanding the difference between what predicts an outcome and what changes it.

  • AI-augmented working style: using AI coding assistants to move faster while sanity-checking their output rather than trusting it by default.

Preferred / Nice to Have
  • Project or coursework experience with recommendation, ranking, or decision-support problems.

  • Familiarity with notebooks-to-production workflows and version control.

  • Exposure to big-data frameworks (Spark, etc.).

Special Factors
Sponsorship
Vanguard is not offering visa sponsorship for this position.
About Vanguard
At Vanguard, we don't just have a mission-we're on a mission.
To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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