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Full Time Nhl Data Science Jobs in Raleigh, NC (NOW HIRING)

Master's degree in Computer Science, Engineering, Data Science or related quantitative field with at least 3 years of relevant full-time experience. * Strong research background in computer vision or ...

W2 only, 12-month contract with potential for extension or conversion to full time with the client ... The candidate will partner closely with Commercial Analytics, Data Science, and Business ...

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Full Time Nhl Data Science information

See Raleigh, NC salary details

$44.7K

$160.4K

$236.7K

How much do full time nhl data science jobs pay per year?

As of Aug 22, 2026, the average yearly pay for full time nhl data science in Raleigh, NC is $160,411.00, according to ZipRecruiter salary data. Most workers in this role earn between $129,800.00 and $165,300.00 per year, depending on experience, location, and employer.

What is a full time NHL data science job?

A Full Time NHL Data Science job involves using statistical analysis, machine learning, and data visualization to help National Hockey League (NHL) teams or organizations make data-driven decisions. Data scientists in this field analyze player performance, game statistics, and other data sources to provide insights that can improve team strategy, scouting, and player development. The role typically requires strong programming skills, knowledge of sports analytics, and the ability to communicate findings to coaches, managers, and executives. Full time positions offer stability, benefits, and the opportunity to work closely with hockey professionals throughout the season.

What are some common challenges faced by data scientists working full-time in the NHL, and how can they be addressed?

One common challenge for NHL data scientists is integrating diverse data sources, such as player tracking, game statistics, and scouting reports, into actionable insights for coaches and management. Balancing real-time analysis with long-term research projects also requires strong time-management and communication skills. Collaborating effectively within multidisciplinary teams—often including coaches, scouts, and IT professionals—helps ensure that complex models translate into practical, game-improving strategies. Staying updated with the latest sports analytics tools and methodologies further supports success in this dynamic role.

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

To thrive as a Full Time NHL Data Scientist, you need a strong background in statistics, data analysis, and programming, typically supported by a degree in mathematics, statistics, computer science, or a related field. Proficiency with data science tools such as Python, R, SQL, and machine learning frameworks, as well as experience with hockey analytics databases and visualization software, is essential. Strong communication, problem-solving, and teamwork skills help convey insights to coaches, analysts, and management effectively. These skills enable data-driven decision-making that can improve team performance and gain a competitive edge in the NHL.

What is the difference between Full Time Nhl Data Science vs Full Time Nhl Data Analyst?

AspectFull Time Nhl Data ScienceFull Time Nhl Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skillsDegree in Analytics, Statistics, or related; basic data skills
Work EnvironmentCollaborative, research-focused, often involves modeling and machine learningOperational, reporting-focused, involves data interpretation and visualization
Employer & Industry UsageUsed by teams for predictive modeling, player performance analysis, strategic decisionsUsed for reporting, data tracking, and supporting game-day decisions

Full Time Nhl Data Science roles focus on advanced analytics, machine learning, and predictive modeling to inform strategic decisions. In contrast, Full Time Nhl Data Analysts primarily handle data reporting, visualization, and supporting day-to-day operations. Both roles require strong analytical skills, but Data Scientists typically have more technical expertise in programming and modeling, while Data Analysts focus on data interpretation and presentation.

What are popular job titles related to Full Time Nhl Data Science jobs in Raleigh, NC?

For Full Time Nhl Data Science jobs in Raleigh, NC, the most frequently searched job titles are:

What job categories do people searching Full Time Nhl Data Science jobs in Raleigh, NC look for?

The top searched job categories for Full Time Nhl Data Science jobs in Raleigh, NC are:

What cities near Raleigh, NC are hiring for Full Time Nhl Data Science jobs?

Cities near Raleigh, NC with the most Full Time Nhl Data Science job openings:

Infographic showing various Full Time Nhl Data Science job openings in Raleigh, NC as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $160,411 per year, or $77.1 per hour.

Data Analytics Business Analyst (Full-Time Remote)

Alliance Health

Morrisville, NC • On-site, Remote

$81K - $104K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 27 days ago


Job description

The Data Analytics Business Analyst gathers and documents business and technical needs for data and analytics projects, converts them into analytical specifications and test plans, assists in building and validating dashboards, reports, and predictive models, and safeguards data quality and HIPAA compliance across all analytical solutions.

This position is full-time remote. Selected candidate must reside in North Carolina and be willing to travel to the home office (Morrisville, NC) for onsite team meetings as needed.

Responsibilities & Duties

Elicit and Document Analytics Requirements

  • Lead discovery meetings to capture business objectives, key performance indicators (KPIs), and reporting needs
  • Capture data source requirements, frequency, granularity, and any service level expectations (e.g., refresh windows)
  • Produce requirements artifacts such as Business Requirements Documents (BRDs), data modeling diagrams, and acceptance criteria that define the desired analytics outcomes
  • Analyze and Profile Data Perform data profiling on source systems (e.g., relational databases, data lakes, SaaS APIs) to understand completeness, consistency, and distribution of fields
  • Conduct gap analysis to identify missing attributes or mismatches against reporting specifications
  • Document data quality issues, propose validation rules, and define reconciliation procedures that support accurate analytics

Translate Requirements into Analytical Specifications

  • Develop detailed functional and technical specifications for data models, dimensional schemas (star/snowflake), and analytical pipelines (ETL/ELT, data wrangling scripts, BI tool configurations)
  • Collaborate with data engineers, data scientists, and BI developers to align design patterns, naming conventions, and reusable components
  • Ensure specifications address scalability, security (including HIPAA related data handling), and maintainability of analytical solutions

Plan and Execute Testing of Analytical Solutions

  • Create test plans, test cases, and validation data sets for unit, integration, and user acceptance testing of dashboards, reports, and predictive models
  • Support business stakeholders with UAT; log defects, prioritize fixes, and oversee retesting cycles
  • Verify performance (e.g., query response time, model runtime) against agreed upon thresholds

Support Implementation and Ongoing Operations

  • Assist with go live activities such as preparation of runbooks, standard operating procedures (SOPs), and cut over checklists for analytics releases
  • Monitor initial production runs, perform data reconciliations, and address any discrepancies that arise
  • Participate in incident response, root cause analysis, and documentation of lessons learned for continuous improvement

Maintain Documentation and Knowledge Base

  • Keep current inventories of data sources, data dictionaries, lineage diagrams, and model documentation up to date
  • Author and refresh end user guides, technical “how to” documents, and metadata catalogs in line with departmental standards

Communication and Collaboration

  • Translate complex analytical concepts into clear language for both technical and non technical audiences
  • Partner with internal business units, external data providers, and vendor teams to ensure alignment on data definitions, delivery schedules, and reporting expectations
  • Contribute to data governance initiatives, supporting standards for data stewardship, privacy, and compliance

Continuous Improvement

  • Identify opportunities to streamline analytics workflows through reusable templates, automation (e.g., CI/CD pipelines for data models), and self service tooling
  • Define and track analytics related KPIs such as report delivery timeliness, data quality error rates, and model accuracy
  • Recommend best practice enhancements to increase efficiency, data reliability, and user satisfaction

Minimum Requirements

Education and Experience

Vocational or Technical Training in in Computer Science, Information Systems, Business Administration, or a related field; and six (6) years of experience in data analytics or data science;

Or

Associate’s degree from an accredited university in Computer Science, Information Systems, Business Administration, or a related field; and five (5) years of experience in data analytics or data science;

Or

Bachelor’s degree from an accredited university in Computer Science, Information Systems, Business Administration, or a related field; and five (3) years of experience in data analytics or data science.

Knowledge, Skills, & Abilities

  • SQL (preferably T-SQL)
  • Communication skills
  • Data Visualization Tools
  • Software Development Life Cycle (SDLC)
  • Data Governance
  • Documentation Tools and Platforms

Employment for this position is contingent upon a satisfactory background check and credit check, which will be performed after acceptance of an offer of employment and prior to the employee's start date. 

Salary Range 

$81,873-104,388/Annually 

Exact compensation will be determined based on the candidate's education, experience, external market data and consideration of internal equity.  

 An excellent fringe benefit package accompanies the salary, which includes:    

  • Medical, Dental, Vision, Life, Long and Short-Term Disability
  • Generous retirement savings plan
  • Flexible work schedules including hybrid/remote options
  • Paid time off including vacation, sick leave, holiday, management leave
  • Dress flexibility