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How to become an NHL data analyst?

To become an NHL data analyst, candidates typically need a strong background in data science, statistics, or related fields, along with proficiency in programming languages like Python or R and experience with sports analytics tools. Knowledge of hockey rules, game strategies, and access to relevant data sources are also important. Gaining experience through internships or projects and staying updated on industry trends can improve job prospects.

How much do NHL data scientists make?

NHL data scientists typically earn between $70,000 and $120,000 annually, depending on experience, education, and the level of responsibility. Salaries can vary based on the organization, location, and whether the role involves advanced analytics, machine learning, or sports-specific data analysis tools.

How much do NFL data scientists make?

NFL data scientists typically earn between $70,000 and $130,000 annually, depending on experience, education, and the complexity of the data analysis tasks. Salaries can vary based on the organization, location, and whether the role involves advanced skills like machine learning or statistical modeling.

How much do NHL data engineers make?

NHL data engineers typically earn between $70,000 and $120,000 annually, depending on experience, education, and location. They work with large datasets, statistical tools, and programming languages like Python or R to analyze hockey data and support team strategies.

What is the difference between Live In Nhl Data Science vs Live In Nhl Data Analysis?

AspectLive In Nhl Data ScienceLive In Nhl Data Analysis
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Data Analysis, Statistics, or related; proficiency in Excel, SQL, and visualization tools
Work EnvironmentCollaborative teams, research-focused, often in tech or sports analytics companiesOperational settings, sports teams, or media outlets focusing on game insights
Employer & Industry UsageTech firms, sports analytics companies, NHL organizationsSports teams, media outlets, NHL organizations

Live In Nhl Data Science involves advanced analytics, machine learning, and predictive modeling to analyze NHL data, while Live In Nhl Data Analysis focuses on interpreting data for insights, reporting, and decision-making. Both roles require familiarity with NHL data but differ in technical complexity and scope.

What cities are hiring for Live In Nhl Data Science jobs? Cities with the most Live In Nhl Data Science job openings:
What are the most commonly searched types of Nhl Data Science jobs? The most popular types of Nhl Data Science jobs are:
What states have the most Live In Nhl Data Science jobs? States with the most job openings for Live In Nhl Data Science jobs include:
Live In Nhl Data Science jobs near you
Data Science Specialist

Data Science Specialist

ANICCA DATA SCIENCE SOLUTIONS LLC

Bellevue, WA • On-site

$87K - $101K/yr

Full-time

Re-posted 15 days ago

Job description

Role Summary
We are looking for an experienced Data Professional to support Azure-based data platform, analytics, reporting, and advanced analytics and science work. The role requires hands-on experience in data engineering, data analysis, business intelligence, and Python-based analytics using Microsoft Azure technologies.
Key Responsibilities
* Design, build, and maintain data pipelines using Azure Data Factory, Azure Data Lake Gen2, Synapse, Fabric, and Databricks.
* Ingest, transform, and validate data from multiple business systems, APIs, databases, and files.
* Develop and maintain data models, source-to-target mappings, business rules, and data quality checks.
* Perform data profiling, reconciliation, gap analysis, and root-cause analysis.
* Write complex SQL, Python, PySpark, and KQL queries for data processing and analysis.
* Build and support Power BI dashboards, reports, semantic models, KPIs, and analytical datasets.
* Apply statistical analysis, forecasting, anomaly detection, and basic machine learning techniques where needed.
* Support Dev/UAT/Prod deployments, CI/CD, documentation, testing, and stakeholder sign-off.
* Ensure data security, governance, RBAC, access control, and performance best practices.
Required Skills
* 5+ years of experience in data engineering, data analytics, BI, or data science.
* Strong hands-on experience with Azure data services.
* Strong skills in SQL, Python, PySpark, Power BI, and data modeling.
* Experience with ADF, ADLS Gen2, Synapse/Fabric, Databricks, dbt, or Airbyte.
* Ability to translate business requirements into technical specifications, data models, reports, and analytics solutions.
* Experience with data validation, documentation, UAT support, and stakeholder communication.
* Knowledge of Azure DevOps, Git, CI/CD, and Agile delivery is preferred.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, Engineering, Statistics, or a related field.