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Remote Football Data Analytics Jobs in Washington

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 ...

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 ...

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 ...

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

Showing results 21-40

Remote Football Data Analytics information

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

AspectRemote Football Data AnalyticsRemote Sports Data Analyst
CredentialsDegree in Data Science, Sports Management, or related field; knowledge of football statisticsDegree in Data Science, Sports Management, or related field; broad sports knowledge
Work EnvironmentRemote, focused on football data projectsRemote, covering multiple sports or general sports data analysis
Industry UsagePrimarily in football clubs, leagues, and football-focused mediaIn various sports organizations, media outlets, and analytics firms
Search & Comparison IntentSpecific to football data analysis rolesBroader sports data analysis roles

Remote Football Data Analytics focuses specifically on football-related data, requiring specialized football knowledge and analytics skills. In contrast, Remote Sports Data Analyst roles encompass multiple sports, demanding broader sports industry understanding. Both roles often share similar credentials and work environments but differ in their scope and industry focus.

What are the most commonly searched types of Football Data Analytics jobs in Washington? The most popular types of Football Data Analytics jobs in Washington are:
What are popular job titles related to Remote Football Data Analytics jobs in Washington? For Remote Football Data Analytics jobs in Washington, the most frequently searched job titles are:
Infographic showing various Remote Football Data Analytics job openings in Washington as of June 2026, with employment types broken down into 96% Full Time, 3% Part Time, and 1% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Senior Data Scientist (NLP and Unstructured Data Analytics)

Node.Digital

Washington, DC • Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 10 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