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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 ...

Department Manager

Chapel Hill, NC

$17.50 - $19.50/hr

... and the Data Science minor. The Ph.D. program provides advanced training in statistics, optimization, probability, and stochastic modeling and typically requires four to five years of full-time ...

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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 Jul 31, 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 July 2026, with employment types broken down into 1% As Needed, 80% Full Time, 10% Part Time, and 9% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $160,411 per year, or $77.1 per hour.

Director, Engineering - AI & Data Science Platform

Fidelity Investments

Durham, NC

$244K/yr

Full-time

Medical, Retirement, PTO

Posted yesterday

New


Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 270 frontline employees who took The Breakroom Quiz

15th of 149 rated financial services


Job description

Job Description:

Note: Fidelity will not provide immigration sponsorship for this position.

The Role

Asset Management Technology is looking for a Director of Engineering to lead a team focused on AI enablement and Data Science platform capabilities across the product area. This leader will manage engineering delivery for platforms and services that enable teams to experiment with, build, deploy, monitor, and scale AI, GenAI, machine learning, and advanced analytics solutions.

This role will provide leadership across the Lumin8 AI enablement ecosystem and the CRISP Data Science Platform. The ideal candidate brings strong engineering leadership, platform product thinking, cloud-native architecture experience, and a passion for enabling technologists, data scientists, quantitative analysts, and business partners to deliver measurable outcomes with responsible, secure, and scalable AI and data science capabilities.

The Expertise You Bring

  • Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent experience.

  • 10 plus years of software engineering, platform engineering, cloud engineering, data science platform, or AI/ML engineering experience.

  • 5 plus years of people leadership experience, including coaching engineers, setting direction, managing delivery, and developing talent.

  • Experience leading cloud-native platforms and services, preferably in AWS, Kubernetes/EKS, CI/CD, observability, and secure enterprise environments.

  • Working knowledge of AI/ML lifecycle capabilities, including experimentation, model development, deployment, monitoring, governance, and support.

  • Experience with GenAI enablement patterns, model gateways, API-based AI services, embeddings, vector stores, LLM integrations, and responsible AI controls.

  • Strong ability to partner with senior stakeholders, translate business and research needs into platform capabilities, and manage competing priorities across multiple product areas.

The Skills You Require

  • You are a strong engineering leader who can build trust, set clear direction, and create a culture of ownership, inclusion, learning, and high-quality execution.

  • You bring platform thinking. You understand how to build reusable capabilities, reduce friction for internal customers, and balance reliability, scalability, usability, and cost.

  • You have strong technical judgment across modern software engineering, cloud infrastructure, data science tooling, APIs, automation, security, and operational excellence.

  • You are comfortable leading teams that support both innovation and production-grade platforms, including experimentation environments, developer frameworks, model deployment paths, and operational support models.

  • You communicate clearly with engineers, data scientists, product owners, architects, risk partners, and senior leaders.

  • You are committed to responsible AI, data protection, enterprise controls, and operating within a highly regulated environment.

The Value You Deliver

  • Lead the engineering team responsible for AI and Data Science platform capabilities across Lumin8 and CRISP.

  • Drive delivery of foundational AI services, developer frameworks, model enablement capabilities, LLM service integrations, embeddings, vector search patterns, and platform support.

  • Advance CRISP capabilities for data scientists, engineers, and quantitative analysts, including compute, storage, MDEs, workspaces, model development, deployment, monitoring, and collaboration workflows.

  • Partner with product and architecture leaders to define the roadmap for AI, GenAI, ML, and advanced analytics platform enablement.

  • Improve developer and data scientist experience through better onboarding, documentation, automation, support, observability, and self-service capabilities.

  • Ensure platforms are secure, resilient, compliant, cost-aware, and aligned with enterprise technology standards.

  • Coach and develop engineers, create clear delivery accountability, and foster a team culture focused on innovation, craftsmanship, and measurable business value.

The Team

The AI and Data Science Platforms team enables modern AI, GenAI, machine learning, and analytics workflows across Asset Management Technology. The team supports foundational AI capabilities through Lumin8, including developer frameworks, model enablement, AI service integration, embedding and vector capabilities, gateway patterns, onboarding, and support. The team also supports CRISP, the Computing Research and Innovation System Platform, which provides compute, storage, tooling, collaboration, model development, deployment, monitoring, and experimentation capabilities for data scientists, engineers, and quantitative analysts.

The team partners closely with engineering, architecture, data science, security, cloud platform, product, and business stakeholders to deliver reliable, compliant, and easy-to-adopt platform capabilities.

Fidelity's Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.

The base salary range for this position is $126,000-255,000 USD per year.

Placement in the range will vary based on job responsibilities and scope, geographic location, candidate's relevant experience, and other factors.

Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.

Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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