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Entry Level Sap Data Scientist Jobs (NOW HIRING)

asrcfh

Data Scientist

Oklahoma City, OK · On-site

Bachelor's degree in Data Science, Computer Science, Computer Engineering, Mathematics or related field Level 1: Entry Level Level 2: Minimum 3 years of experience equivalent to a level 1 Level 3: ...

Noblesoft Solutions

Data Scientist

Jacksonville, FL

This is an entry level Data Scientist Role for a person who is self motivated and has a passion to innovate and work on cutting edge technology. Qualifications Candidate should have experience in ...

Noblesoft Solutions

Data Scientist

Jacksonville, FL · On-site

This is an entry level Data Scientist Role for a person who is self motivated and has a passion to innovate and work on cutting edge technology. Qualifications Candidate should have experience in ...

ASRC Federal Holding

Data Scientist

Oklahoma City, OK · On-site

Bachelor's degree in Data Science, Computer Science, Computer Engineering, Mathematics or related field Level 1: Entry Level Level 2: Minimum 3 years of experience equivalent to a level 1 Level 3: ...

Paramount

Associate Data Scientist

Manhattan, NY

$64K - $65K/yr

We are seeking an Associate Data Scientist for this entry-level role. You will work to support the team in building ML-powered analyses and products that shape business strategy, optimize content ...

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Entry Level Sap Data Scientist information

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$46K

$165K

$243.5K

How much do entry level sap data scientist jobs pay per year?

As of Jul 18, 2026, the average yearly pay for entry level sap data scientist in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is the salary of a data scientist in SAP?

The salary of an SAP data scientist varies based on experience, location, and certifications but typically ranges from $70,000 to $120,000 annually. Entry-level roles may start lower, while experienced professionals with SAP and data science skills can earn higher salaries.

What types of projects do entry-level SAP Data Scientists typically work on, and how do they contribute to the overall business goals?

Entry-level SAP Data Scientists often work on projects involving data extraction, transformation, and analysis within SAP environments. They typically support senior data scientists by preparing large datasets, building basic models, and generating reports that help stakeholders make informed decisions. Their contributions are crucial in streamlining business processes, identifying patterns, and improving operational efficiency. Collaboration is common with SAP consultants, IT teams, and business analysts to ensure data-driven solutions align with organizational objectives.

What are the key skills and qualifications needed to thrive as an Entry Level SAP Data Scientist, and why are they important?

To thrive as an Entry Level SAP Data Scientist, you need a strong background in statistics, data analysis, and programming (often in Python or R), typically supported by a relevant degree in computer science, data science, or a related field. Familiarity with SAP analytics tools (like SAP HANA, SAP BW, or SAP Analytics Cloud), as well as data visualization platforms and SQL, is commonly expected. Analytical thinking, problem-solving, and effective communication are essential soft skills that help in interpreting data and presenting findings to stakeholders. These skills and qualifications are crucial for extracting actionable insights from data, supporting business decisions, and succeeding in a technology-driven environment.

What is the difference between Entry Level Sap Data Scientist vs Entry Level SAP Analyst?

AspectEntry Level Sap Data ScientistEntry Level SAP Analyst
Required CredentialsBachelor's in Data Science, Computer Science, or related field; basic knowledge of SAP modules; familiarity with data analysis toolsBachelor's in Information Systems, Business, or related field; basic SAP certification; understanding of SAP modules and business processes
Work EnvironmentData analysis, modeling, and visualization tasks within IT or analytics teamsSystem configuration, support, and process analysis within SAP support teams
Employer & Industry UsageTech companies, consulting firms, industries using SAP for data insightsManufacturing, finance, logistics, and other industries utilizing SAP ERP systems

In summary, Entry Level SAP Data Scientists focus on analyzing SAP data and developing models, while Entry Level SAP Analysts support SAP systems and optimize business processes. Both roles require familiarity with SAP but differ in their core responsibilities and skill sets.

Is 40 too late for data science?

Entry level data science roles typically require foundational skills in programming, statistics, and data analysis, which can be acquired at any age. Age is not a barrier; success depends on relevant skills, certifications, and experience, regardless of when you start learning. Many professionals transition into data science later in their careers by gaining necessary knowledge and practical experience.

Can I get a SAP job with no experience?

Entry level SAP data scientist roles typically require some knowledge of SAP systems, data analysis, and programming skills such as SQL or Python. While prior experience is often preferred, candidates with relevant certifications, strong analytical skills, and a willingness to learn can sometimes qualify for entry-level positions or internships in this field.

What does an Entry Level SAP Data Scientist do?

An Entry Level SAP Data Scientist is responsible for analyzing large sets of data within SAP systems to help organizations make data-driven decisions. They use statistical methods, machine learning, and data visualization tools to identify patterns, trends, and insights from business data. Typically, they work closely with business analysts and IT teams to develop and implement data models, automate data processing, and generate reports that support business objectives. This role often involves cleaning and preparing data, as well as learning SAP-specific analytics tools like SAP HANA and SAP Predictive Analytics.

Can I get a data scientist job with no experience?

Entry-level data scientist positions typically require some knowledge of programming languages like Python or R, and familiarity with data analysis tools and techniques. While prior work experience is not always necessary, candidates often need relevant coursework, certifications, or personal projects to demonstrate their skills. Building a strong foundation in statistics, machine learning, and data visualization can improve chances of securing an entry-level role without professional experience.
More about Entry Level Sap Data Scientist jobs
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Entry Level Sap Data Scientist jobs near you
Infographic showing various Entry Level Sap Data Scientist job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Data Scientist

Data Scientist

Zimmerman Advertising

Fort Lauderdale, FL

Full-time

Posted 23 hours ago

Job description

The Data Scientist works closely with Retail Technology, Media and Account Services teams to provide predictive modeling of Marketing, Direct & Digital Efforts. We are looking for a motivated Data Scientist and analytical thought leader. This is a rare opportunity to be part of a diverse and newly expanded analytics department and a great fit for a predictive modeler with a desire to impact business results.

Responsibilities

  • Apply specialized technical knowledge and expertise to perform reviews relating to the full life cycle of models, information technology applications, or risk management/analysis used across the company.
  • Collaborate and share knowledge with teams across the media organization, as appropriate. Build and maintain relationships with business partners at the manager and staff levels.
  • Use data analysis, mining, and migration techniques for enhanced targeting, audience segmentation, clustering, profiling, and regression analysis
  • Identify digital placement-level strengths and weaknesses across simultaneous campaigns and geographies
  • Develop and maintain internal automated reporting tools, documents, scoring systems, and dashboards for on-going and post-campaign reporting
  • Coordinate cross-functional reviews to discuss region- and campaign-specific findings and actionable recommendations for digital media campaigns built on various CPM, CPC, CPE, and CPA models
  • Identify and facilitate resolution of tagging issues in coordination with Traffic and Production teams focused on site-side tracking, reporting, and implementation
  • Provide client-facing/non-technical recommendations and insights, both in a written and verbal manner, that provide understandable and actionable optimizations.
  • Work with Media, Strategic Intelligence and Account Services teams to develop measurement plans to deliver on campaign and client objectives

Requirements

  • Bachelor's degree in related field
  • Entry-Level and/or College Internship experience 
  • Must demonstrate the ability to successfully develop and run analytics (scripts) using specialized tools and platforms, specifically, R, Python, SQL, and/or SAS.
  • Experience applying data synthesis, mining and regression techniques for enhanced targeting, audience segmentation, clustering, profiling, and insightful recommendations
  • Advanced knowledge of Microsoft Excel
  • General understanding of digital advertising, digital media strategy, ad placement type, placement-level insight, and standard media metrics is preferred
  • Experience with data orchestration tools such as Annalect Omni is preferred
  • Excellent verbal, written and interpersonal communication skills
  • Ability to work independently and as part of a team
  • Ability to manage multiple projects simultaneously while meeting deadlines
  • Regression modeling focusing on maximizing yield while measuring the diminishing returns of ad spend at scale for thousands of locations.
  • Data storytelling and presentation

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