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Graph Algorithm Scientist Jobs (NOW HIRING)

Dynata

The Senior Lead Data Scientist will be responsible for designing and optimizing graph-based ... Deep expertise in graph analytics, including graph databases, graph algorithms, similarity modeling ...

Point72

Data Engineer, Knowledge Graph

New York, NY · On-site

$125K - $150K/yr

Our industry researchers, product managers, and data scientists and engineers work together to ... algorithms to uncover hidden patterns and relationships in financial data. * You excel at ...

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Graph Algorithm Scientist information

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$50.5K

$111.3K

$137.5K

How much do graph algorithm scientist jobs pay per year?

As of Sep 11, 2026, the average yearly pay for graph algorithm scientist in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

What does a graph algorithm scientist do?

A Graph Algorithm Scientist specializes in developing and optimizing algorithms that analyze and extract insights from graph-structured data. This involves working with data that can be represented as nodes and edges, such as social networks, recommendation systems, or biological networks. Their work includes designing new algorithms, improving computational efficiency, and applying graph theory to solve complex real-world problems. They often collaborate with software engineers and data scientists to implement these algorithms in scalable systems.

What are the key skills and qualifications needed to thrive as a graph algorithm scientist?

A Graph Algorithm Scientist typically needs a strong background in mathematics, computer science, and algorithm design, often supported by an advanced degree (Master’s or PhD) in a related field. Proficiency with programming languages such as Python, C++, and frameworks like NetworkX or Neo4j, as well as familiarity with big data platforms, is essential. Strong analytical thinking, problem-solving ability, and effective communication make candidates stand out in this role. These skills enable the design, implementation, and interpretation of complex graph-based solutions that drive innovation in areas like recommendation systems, network analysis, and data mining.

What are some typical challenges a graph algorithm scientist faces when working with large-scale data?

Graph Algorithm Scientists often encounter challenges related to the scalability and efficiency of algorithms when dealing with massive datasets. Real-world graphs, such as social networks or recommendation systems, can contain millions or even billions of nodes and edges, making it essential to optimize both memory usage and computational speed. Another common challenge is ensuring data quality and dealing with incomplete or noisy data, which can impact the accuracy of graph-based models. Collaborating closely with data engineers and software developers is key to deploying robust, production-ready solutions.

What are popular job titles related to Graph Algorithm Scientist jobs?

For Graph Algorithm Scientist jobs, the most frequently searched job titles are:

Infographic showing various Graph Algorithm Scientist job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 10% Part Time, and 4% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.

Graph Data Scientist

Charlotte, NC • On-site

Inficare Technologies
Recruiting and Staffing Services • 51 - 200 employees
Learn more about Inficare Technologiesexternal

Full-time

This job post has expired 3 days ago. Applications are no longer accepted.

Job description

Job Title: Graph Data Scientist
Location: Charlotte, NC (Day 1 Onsite)
Job Type: Contract
Experience: Mid-Senior level
Job Overview
Our client is looking for a highly skilled Graph Data Scientist to design and develop graph-based analytical solutions leveraging Neo4j/TigerGraph and advanced data science techniques. The role will focus on building knowledge graphs, detecting complex patterns, and enabling AI-driven insights across enterprise use cases.
Key Responsibilities
  • Design and develop graph data models using Neo4j or TigerGraph
  • Build and manage knowledge graphs for enterprise use cases including fraud detection, relationship mapping, and pattern discovery
  • Apply data science and ML techniques to extract actionable insights from graph data
  • Develop and optimize graph algorithms (pathfinding, centrality, clustering, anomaly detection)
  • Integrate graph platforms with Gen AI / LLM-based systems for intelligent decision-making
  • Collaborate with engineering and AI teams to embed graph insights into production applications
Required Skills
  • Strong hands-on experience with Neo4j, TigerGraph, or similar graph databases
  • Solid background in data science: Python, Pandas, Scikit-learn, PyTorch / TensorFlow
  • Experience in graph algorithms and network analysis
  • Hands-on with data modeling, ETL, and large-scale data processing
  • Understanding of Gen AI / LLM integration with structured data systems
Preferred Skills
  • Experience with knowledge graphs and semantic data models
  • Exposure to hybrid architectures combining graph databases with AI systems
Location & Work Model
Charlotte, NC - Onsite from Day 1. Candidates must be willing and able to work on-site immediately.
Engagement Details
Contract engagement. Two openings available. Immediate start preferred.

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