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Data Science And Analytics Jobs (NOW HIRING)

Shamrock

Data Science Analyst

Phoenix, AZ · On-site

The Data Science Analyst is responsible for using data science, machine learning, statistical ... Support transportation and logistics optimization initiatives by applying advanced analytics and ...

Shamrock Foods Company

Data Science Analyst

Phoenix, AZ · On-site

$95 - $135/hr

The Data Science Analyst is responsible for using data science, machine learning, statistical ... Support transportation and logistics optimization initiatives by applying advanced analytics and ...

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How much do data science and analytics jobs pay per year?

As of Aug 23, 2026, the average yearly pay for data science and analytics in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is data science and analytics?

Data Science and Analytics refer to the fields that focus on extracting meaningful insights from large and complex data sets. Data Science combines statistics, computer science, and domain knowledge to analyze data, build predictive models, and support data-driven decision-making. Analytics, which is a core part of data science, involves examining data to discover trends, patterns, and correlations that can help organizations solve problems or improve processes. Professionals in these fields use tools such as Python, R, SQL, and machine learning algorithms to analyze data and communicate findings to stakeholders.

What are some common challenges faced by data science and analytics professionals when working with cross-functional teams?

Data science and analytics professionals often collaborate with colleagues from diverse backgrounds such as engineering, marketing, and business operations. One common challenge is translating complex analytical findings into actionable insights that non-technical stakeholders can easily understand. Additionally, aligning project objectives and timelines across teams can require strong communication and project management skills. Overcoming these challenges is essential for ensuring that data-driven solutions are effectively implemented and contribute to organizational goals.

What are the key skills and qualifications needed to thrive as a data science and analytics professional, and why are they important?

To thrive in Data Science and Analytics, you need strong skills in statistics, data manipulation, and programming, typically backed by a degree in computer science, mathematics, or a related field. Proficiency with tools like Python, R, SQL, and data visualization platforms such as Tableau, along with knowledge of machine learning frameworks, is highly valued. Strong problem-solving ability, critical thinking, and effective communication skills help translate complex data findings into actionable business insights. These skills are crucial for turning raw data into strategic decisions that drive organizational success.

What is the difference between Data Science And Analytics vs Data Analysis?

AspectData Science And AnalyticsData Analysis
Required SkillsStatistical modeling, programming, machine learningData cleaning, descriptive statistics, visualization
Work EnvironmentCross-functional teams, R&D, predictive modelingBusiness reporting, dashboards, ad hoc analysis
Tools & TechnologiesPython, R, SQL, Hadoop, SparkExcel, SQL, Tableau, Power BI
Industry UsageTech, finance, healthcare, marketingRetail, finance, healthcare, operations

Data Science And Analytics involves advanced techniques like machine learning and predictive modeling, often requiring programming skills. Data Analysis focuses on interpreting existing data through descriptive statistics and visualization for decision-making. Both roles are essential but differ in complexity and scope.

What can I do with data science and analytics?

Data science and analytics professionals analyze large datasets to extract insights, support decision-making, and improve business processes. They use tools like Python, R, and SQL, and often work in environments that require strong statistical and programming skills. These roles can lead to careers in industries such as finance, healthcare, marketing, and technology, with opportunities for advancement and specialization.

What jobs can I get with a data science and analytics degree?

A degree in data science and analytics can lead to roles such as data analyst, data scientist, business intelligence analyst, machine learning engineer, and data engineer. These positions typically require skills in programming languages like Python or R, data visualization tools, and statistical analysis, often with certifications or experience in big data platforms and SQL. Job responsibilities include interpreting complex data, building predictive models, and supporting data-driven decision-making.
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What states have the most Data Science And Analytics jobs?

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Data Science And Analytics jobs near you
Infographic showing various Data Science And Analytics job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

PEGA Data Science & Analytics

INSPYR Solutions

Merrifield, VA • On-site

$56 - $73.50/hr

Other

Medical, Retirement

Re-posted 11 days ago

Job description

Title: PEGA Data Science & Analytics
Location: Vienna, VA (HYBRID)
Duration: 6-month contract with possible extensions
Work Requirements: , Holders or Authorized to Work in the U.S.
 
PEGA Data Science & Analytics
To enable and simplify analysis required for faster implementation of new CDH modeling features. Looking for a candidate with 5-10 years of experience + Master''s degree and 1-2 years of strong data science and coding experience.
Prioritized Deliverables:
1. Library of required queries/scripts to replicate the CDH customer contextual object in external systems (databricks/asl) for deeper analysis
2. Standardize format for executing key data retrieval steps for use by the broader team
a. Interaction to outcome attribution (account opens)
b. Model data to interaction mapping (model performance, predictor performance)
c. Member Profile to interaction mapping
3. Create notebooks for the broader team to use to answer specific questions
a. Distribution Analysis
b. Arbitration Analysis
c. Channel Engagement Analysis

Skillset:
The primary technical skills required would be familiarity with the databricks environment and proficiency with Python/PySpark and SQL. Pega CDH experience is preferred.
Some examples of the work as it directly relates to GEM
• Initial Analysis to Support New Model Related Features
o Propensity Thresholds
▪ Creating the back-testing approach (MDSA had no appetite at the time)
▪ Establishing baseline KPIs
▪ Creating the monitoring approach
o Initial Model Maturity Analysis (though Morgan’s team is starting to be involved)
▪ Establishing baseline KPIs
▪ Gauging the impact of enabling the feature
▪ Creating the ongoing monitoring approach
• On-going Analysis
o Model Performance Monitoring
▪ Though MDSA owns the code to run the notebooks, when changes must be made to the code GEM is heavily involved in creating the new logic
o NBI Program Model Health
▪ This exists in some form today, but it is not in a state that is readily available to be shared with leaders in O&A, MDSA, or broader Marketing
Broader O&A Analytical Gaps: (Things Red, Sumant, and Tai typically scramble to create which should be readily available)
• “Actionable Monitoring Data:” Standardizing how we conduct this sort of analysis for consistency
o Capture when propensity scores are exceptionally low closer to real-time (1 day)
o Capture when actions are not providing value to their intended objective (acquisition, engagement)
• Eligible Audience Monitoring
o Identifying Members eligible for different actions/treatments (simulation environment can help after going live to a certain extent)
o Tying interactions back to key Member demographic data for more granular analysis (this sort of analysis should be standardized so it can easily be done by all Members of O&A)

Our benefits package includes:
  • Comprehensive medical benefits
  • Competitive pay
  • 401(k) retirement plan
  • …and much more!
 
About INSPYR Solutions
Technology is our focus and quality is our commitment. As a national expert in delivering flexible technology and talent solutions, we strategically align industry and technical expertise with our clients’ business objectives and cultural needs. Our solutions are tailored to each client and include a wide variety of professional services, project, and talent solutions. By always striving for excellence and focusing on the human aspect of our business, we work seamlessly with our talent and clients to match the right solutions to the right opportunities. Learn more about us at inspyrsolutions.com.
 
INSPYR Solutions provides Equal Employment Opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. In addition to federal law requirements, INSPYR Solutions complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities.
 

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