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Remote Nhl Data Analytics Jobs in Washington (NOW HIRING)

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Provide consulting relating to the data mining and analysis of data from a range of sources to ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Provide consulting relating to the data mining and analysis of data from a range of sources to ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

Maximus

Data Scientist II (Remote)

VA · On-site +1

General information Job Posting Title Data Scientist II (Remote) Date Tuesday, August 4, 2026 City ... This role applies advanced analytics and research methods to help improve the short- and long-term ...

kgs

Data & Metrics Analyst (REMOTE)

Chantilly, VA · Remote

... and analytics lead responsible for developing, maintaining, and continuously improving the program's tracking databases, performance dashboards, and metrics reporting capabilities--directly ...

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Remote Nhl Data Analytics information

What is a remote NHL data analytics job?

Remote NHL Data Analytics jobs involve analyzing hockey-related data for the National Hockey League (NHL) while working from a remote location. Professionals in this field collect, organize, and interpret large datasets to provide insights into player performance, team strategies, and game outcomes. The work may include statistical modeling, data visualization, and the use of specialized software to help teams, media, or betting companies make informed decisions. These positions typically require strong analytical skills, proficiency in programming languages like Python or R, and a deep understanding of hockey. Remote roles allow for flexibility in location while still collaborating with teams and organizations virtually.

What are some common challenges faced by professionals in a remote NHL data analytics role, and how can they be addressed?

A common challenge in remote NHL Data Analytics is ensuring seamless collaboration with coaches, scouts, and other analysts despite being physically distant from the team. Clear communication and regular virtual meetings are essential to align on objectives and share insights. Another challenge is managing and accessing large, proprietary datasets securely from remote locations, which requires strong data management practices and familiarity with secure cloud platforms. Staying updated on the latest hockey analytics trends and tools is also important for delivering actionable insights to the team.

What is the difference between Remote Nhl Data Analytics vs Remote Sports Data Analyst?

AspectRemote Nhl Data AnalyticsRemote Sports Data Analyst
CredentialsDegree in Data Science, Statistics, or Sports ManagementDegree in Data Science, Statistics, or Sports Management
Work EnvironmentRemote, sports analytics companies, NHL teamsRemote, sports organizations, analytics firms
Industry UsagePrimarily NHL, hockey-focused analyticsVarious sports including hockey, football, basketball
Job FocusHockey-specific data analysis, player performance, game strategiesMultiple sports data analysis, performance metrics, trend forecasting

Remote Nhl Data Analytics specializes in hockey-specific data analysis within the NHL industry, focusing on player performance and game strategies. In contrast, Remote Sports Data Analyst roles cover multiple sports, analyzing broader performance metrics across various leagues. Both roles require similar credentials and often share work environments, but their industry focus and data scope differ significantly.

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

To thrive as a Remote NHL Data Analyst, you need a strong foundation in statistics, data analysis, and hockey knowledge, typically supported by a degree in data science, statistics, or a related field. Familiarity with analytics tools like Python, R, SQL, and visualization platforms such as Tableau or Power BI is essential, along with experience using sports data sources and APIs. Excellent problem-solving, communication, and self-motivation are crucial soft skills for collaborating with remote teams and conveying insights to stakeholders. These skills and qualities are important for producing accurate, actionable insights that drive team strategy and performance improvements in a fast-paced, competitive sports environment.
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Remote Nhl Data Analytics jobs near you

Senior Data Scientist (NLP and Unstructured Data Analytics)

Node.Digital

Washington, DC • Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Job description

Senior Data Scientist (NLP and Unstructured Data Analytics)

Location: Herndon, VA (Remote Work)

Must have a Public Trust Clearance

KEY RESPONSIBILITIES

  • Integrate and scale natural language processing methods to parse, clean, and analyze large corpora of unstructured and semi structured text, using optical character recognition, semantic similarity algorithms, and large language models as needed.
  • Design, develop, test, calibrate, and implement statistical and machine learning models targeting financial fraud, improper payments, and non compliance within SBA programs.
  • Build and refine supervised and unsupervised models, including regression, Bayesian, clustering, and ensemble approaches.
  • Review, maintain, and support all existing loan fraud indicators developed by TSD.
  • Perform data quality analysis on source tables and develop repeatable processes for combining and analyzing large data sources.
  • Collaborate directly with criminal investigators to determine and execute analytic strategies supporting loan fraud cases, and adhere closely to the federal rules of criminal procedure governing protected information, including Rule 6(e).
  • Develop case leads for SBA OIG investigations from model outcomes.
  • Document all methodology, test models, and production models in a form that satisfies criminal evidentiary requirements.
  • Build visualizations and dashboards conveying methodological choices, outcomes, and predictive capability, iterated on end user feedback.
  • Deliver findings in multiple registers: data summaries and visualizations for investigative staff, executive summaries for OIG leadership.
  • Coordinate with the data engineering seat so the architecture supports machine learning and text processing pipelines efficiently.
  • Create programming and automation techniques using SharePoint, Python, Excel, Power BI, Power Apps, and similar tools.
  • Identify new business questions that expand the scope of analysis and reporting.

Requirements

Required

Education

Master's, Ph.D., or doctorate level equivalent degree in data science, machine learning, computer science, mathematics, or a related field. Alternatively, ten years of applied work experience in any of the same fields.

  • 5+ yearsDesigning, implementing, and maintaining advanced AI systems and predictive models, including both supervised and unsupervised models.
  • 5+ yearsDeveloping analytic rules and models using leading edge analytic tools and best practices.
  • 5+ yearsDeveloping regression, classification, and other statistical models to identify anomalies, patterns, and predictive variables.
  • 3+ yearsProviding data support for criminal investigations into financial fraud or abuse of government funds.
  • 3+ yearsManipulating data in Python. Pandas is required.
  • 3+ yearsWorking in a modern cloud environment: Azure, AWS, or GCP. Certifications preferred.
  • 2+ yearsConducting advanced data analysis in SQL, specifically SQL Server and PostgreSQL.
  • 2+ yearsDeveloping and scaling natural language processing solutions.
  • 2+ yearsPresenting methods and findings to technical and non technical stakeholders, both orally and in written products and visualizations.

PREFERRED QUALIFICATIONS

  • Production experience with named entity recognition and entity resolution across messy document corpora.
  • Retrieval augmented generation, vector stores, embeddings, and semantic search at scale.
  • Large language model integration under federal security constraints, including boundary controlled deployment and prompt versioning.
  • Optical character recognition pipelines applied to scanned or low quality source documents.
  • Topic modeling, document classification, or clustering applied to audit, legal, or investigative text.
  • Cloud certification in Azure, AWS, or GCP.

Benefits

We are proud to offer competitive compensation and benefits packages to include

  • Medical
  • Dental
  • Vision
  • Basic Life
  • Health Saving Account
  • 401K matching
  • Three weeks of PTO/Sick
  • 11 Paid Holidays
  • Pre-Approved Online Training