1

Data Science Jobs in West Virginia (NOW HIRING)

Advanced degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field * 5+ years in data science or analytical research roles, with a track record of delivering ...

Data Science and Data Engineering Job Qualifications: Skills: Analytical Thinking, Data Analytics, Team Leadership Certifications: None Experience: 5 + years of related experience US Citizenship ...

Required : • Bachelor's degree in Computer Information Technology, Data Science, Statistics or a related field • 3 year + Database experience - SQL/Query design and execution. • 2 year ...

Showing results 41-60

Data Science information

See West Virginia salary details

$29K

$95K

$152.1K

How much do data science jobs pay per year?

As of Sep 6, 2026, the average yearly pay for data science in West Virginia is $95,020.00, according to ZipRecruiter salary data. Most workers in this role earn between $76,300.00 and $105,300.00 per year, depending on experience, location, and employer.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

Is a data scientist in high demand?

Data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What jobs can a data scientist do?

A data scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.

What are the most commonly searched types of Data Science jobs in West Virginia?

The most popular types of Data Science jobs in West Virginia are:

What are popular job titles related to Data Science jobs in West Virginia?

For Data Science jobs in West Virginia, the most frequently searched job titles are:

What cities in West Virginia are hiring for Data Science jobs?

Cities in West Virginia with the most Data Science job openings:

Infographic showing various Data Science job openings in West Virginia as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $95,020 per year, or $45.7 per hour.

Full-time

Medical, Retirement, PTO

Re-posted 2 days ago


Key responsibilities

  • Design and build new measurement products by combining Nielsen datasets across various media types.

  • Develop statistical and machine learning models to address questions beyond standard products.

  • Write production-quality SQL, Python, and PySpark to extract, transform, and model data from Nielsen's cloud environment.


Job description

Company Description

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it's consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Job Description

The Custom Media Analytics Delivery team sits within Nielsen's Commercial organization and functions
as an incubator for new innovative products. We leverage existing Nielsen datasets to build custom
solutions that don't yet exist in Nielsen's standard product portfolio. That means taking raw, often messy
datasets from across Nielsen's ecosystem and turning them into something a client can actually use -
which requires knowing the data deeply, modeling it correctly, and moving fast.

What You'll Do

  • Design and build new measurement products by combining Nielsen datasets across National and Local linear TV, Streaming, Audio, and Digital - understanding the weighting rules, projection logic, and methodology differences that make cross-dataset work hard to get right
  • Develop statistical and machine learning models that extend or adapt Nielsen methodologies to answer questions that standard products can't address
  • Write production-quality SQL, Python, and PySpark to extract, transform, and model data from

Nielsen's cloud data environment

  • Build reusable data pipelines and ETL workflows that allow custom solutions to be delivered repeatably rather than rebuilt from scratch each time
  • Use AI tools actively - to validate models, accelerate pipeline development, stress-test logic, and compress the time between concept and delivery
  • Translate stakeholder requests into well-scoped analytical problems, push back when the ask is unclear, and deliver with clear documentation of methodology and assumptions
  • Collaborate with Research, Commercial Sales, and Client Insights teams; communicate complex model decisions in plain language
Qualifications

Nielsen & Media Research Knowledge

  • 3+ years working directly with Nielsen datasets (TAM, DAR/N1Ads, DCR, Audio, or similar) with hands-on knowledge of Nielsen's weighting, projection, and audience estimation methodology
  • Proven ability to merge and reconcile multiple Nielsen data sources, navigating differences in sample design, universe estimates, and reporting conventions
  • Solid grasp of US media research fundamentals across Television, and Digital

Data Science & Modeling

  • Advanced degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field
  • 5+ years in data science or analytical research roles, with a track record of delivering production-grade models - not just analyses
  • Strong foundations in statistical modeling, sampling theory, weighting, and survey-based projections; comfortable with ML techniques where they fit
  • Engineering & Technical Stack
  • Expert-level Python and SQL; strong PySpark for big data work in cloud environments (AWS preferred)
  • Experience with Databricks for large-scale data processing and ML workflows; familiarity with warehouse-native ML (Databricks ML, Snowflake, or BigQuery ML) is a plus
  • Experience building and maintaining ETL pipelines using Airflow or equivalent orchestration tools
  • Familiarity with data warehousing concepts, cloud-native storage (Redshift, S3, or similar), and data engineering principles

AI Fluency - Required, Not Optional

  • Active daily use of AI tools (LLMs, copilot-style assistants) for code generation, model validation, documentation, and workflow acceleration - this is a core expectation of the role
  • Experience designing agentic AI workflows for automation - chaining tools, validation steps, and outputs to reduce manual effort on repeatable tasks
  • Comfortable evaluating where AI outputs need verification versus where they can be trusted; understands the limits as well as the leverage

Communication & Delivery

  • Ability to explain methodology decisions to non-technical stakeholders without oversimplifying the tradeoffs
  • Strong documentation habits - methods, data dictionaries, and assumptions written up so others can reproduce and build on your work
  • Proficiency in Tableau, Spotfire, or equivalent visualization tools for QA and client-facing output

This role is for someone who is energized by building things that don't exist yet, comfortable in ambiguity, and
disciplined enough to get the methodology right the first time.

#LI-LS1

Additional Information

Enabling your best to power a better media future.

Holistic Rewards: We are committed to an inclusive benefits package that supports our employees and their families. This includes comprehensive health and wellness plans, a 401(k) with a Nielsen company match, and a generous paid time off policy. Depending on the role, additional benefits may include a company-provided vehicle and/or discretionary incentive/bonus eligibility.

Compensation Transparency: The posted base salary range is a reasonable estimate that  may be adjusted based on the final work location of the selected employee. Individual pay within the range is determined by factors such as experience, training, geography, certifications, and business needs. Beyond base salary, this role may be eligible for bonuses, equity, or other incentives.

Nielsen makes hiring decisions without regard to disability status, protected veteran status, or membership in any other protected class.

Please be aware that job-seekers may be at risk of targeting by scammers seeking personal data or money. Nielsen recruiters will only contact you through official job boards, LinkedIn, or email with a nielsen.com domain. Be cautious of any outreach claiming to be from Nielsen via other messaging platforms or personal email addresses. Always verify that email communications come from an @nielsen.com address. If you're unsure about the authenticity of a job offer or communication, please contact Nielsen directly through our official website or verified social media channels.