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Entry Level Artificial Intelligence Finance Jobs

CBIZ

Practice Operations | Artificial Intelligence

Coordinate across delivery, sales, finance, marketing, and shared services teams to support practice needs, resource alignment, and issue resolution. * Help support onboarding, training coordination ...

Aston Carter

Entry Level Financial Analyst

Columbia, MD · On-site

$23/hr

Strong skills in business analysis, finance, and data analysis. * Ability to research and interpret ... Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of ...

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How much do entry level artificial intelligence finance jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for entry level artificial intelligence finance in the United States is $20.40, according to ZipRecruiter salary data. Most workers in this role earn between $17.07 and $22.84 per hour, depending on experience, location, and employer.

What is an entry level artificial intelligence finance job?

Entry level artificial intelligence finance jobs are positions for recent graduates or those new to the field, where they apply AI and machine learning techniques to solve financial problems or improve financial services. Roles may include data analyst, machine learning engineer, or quantitative analyst, typically within banks, investment firms, or fintech companies. Responsibilities often involve analyzing financial data, building predictive models, automating trading strategies, or detecting fraud using AI tools. These jobs usually require a background in mathematics, statistics, computer science, or finance, along with programming skills in languages like Python or R. Entry level positions provide hands-on experience and are a stepping stone to more advanced roles in AI-driven finance.

What are the key skills and qualifications needed to thrive as an entry level artificial intelligence finance professional?

To thrive as an Entry Level Artificial Intelligence Finance professional, you typically need a solid grounding in finance, statistics, and programming, often supported by a degree in finance, computer science, or a related field. Familiarity with data analysis tools (such as Python, R, and Excel), machine learning libraries (like TensorFlow or scikit-learn), and financial modeling software is important. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex data and collaborate with diverse teams. These competencies are crucial for leveraging AI to create financial insights and drive data-driven decision-making in a rapidly evolving industry.

What types of projects can I expect to work on as an entry level artificial intelligence finance professional?

As an entry-level AI finance professional, you can expect to work on projects such as building predictive models for credit risk assessment, automating data analysis tasks, or assisting in the development of algorithmic trading strategies. You'll likely collaborate with data scientists, financial analysts, and software engineers to collect and preprocess financial data, test machine learning models, and interpret results for business insights. This role offers exposure to both technical and finance domains, providing valuable experience for future growth into more specialized or leadership positions.

What is the difference between Entry Level Artificial Intelligence Finance vs Entry Level Data Analyst?

AspectEntry Level Artificial Intelligence FinanceEntry Level Data Analyst
Required CredentialsBachelor's in Finance, Computer Science, or related field; familiarity with AI toolsBachelor's in Statistics, Mathematics, or related field; proficiency in data analysis software
Work EnvironmentFinance firms, tech companies, or financial departments using AI modelsBusiness, finance, healthcare, or marketing sectors analyzing data sets
Employer & Industry UsageFinancial institutions integrating AI for predictive analytics and risk assessmentOrganizations seeking insights from data to inform decision-making

Entry Level Artificial Intelligence Finance roles focus on applying AI techniques within financial contexts, requiring knowledge of finance and AI tools. Entry Level Data Analyst positions involve analyzing data across various industries, emphasizing statistical skills. While both roles involve data handling, AI Finance emphasizes AI applications in finance, whereas Data Analysts focus on interpreting data to support business decisions.

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Entry Level Artificial Intelligence Finance jobs near you
Infographic showing various Entry Level Artificial Intelligence Finance job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution, with an average salary of $42,427 per year, or $20.4 per hour.

Artificial Intelligence (AI) Analyst

Factory Motor Parts

Eagan, MN

$100K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 4 days ago

Factory Motor Parts rating

6.3

Company rating: 6.3 out of 10

Based on 76 frontline employees who took The Breakroom Quiz

305th of 365 rated logistics

Job description

Factory Motor Parts is moving its AI and data platform past reporting and into production systems: retrieval over unstructured company documents, agents that query live business data, and automations that push AI output into daily operations. The AI Analyst builds and supports those systems end to end - retrieval pipelines, the agent and tool layer, the data models underneath them, and the evaluation and monitoring that keep the output trustworthy enough for people to act on.

 

This is a hands on, build focused role on a small team with wide scope. The right person writes Python and SQL comfortably, thinks clearly about how data is modeled, and iterates fast on systems where correctness matters more than novelty.

KEY RESPONSIBILITES

  • Design, build, and maintain RAG pipelines over FMP's internal documents - chunking, embedding, indexing, and hybrid retrieval - and continuously improve retrieval quality against measured baselines
  • Develop agent workflows and MCP tools that give AI governed, secure access to live FMP business data, including text-to-SQL against the analytics warehouse
  • Build and maintain ELT pipelines and dbt models that land and curate data in FMP's data lake for both analytics and AI consumption
  • Build and maintain production automations (n8n) that connect AI output to daily processes across finance, operations, sales, and warehouse teams
  • Build evaluation frameworks, monitoring, and cost controls so AI output is measured and trustworthy before it reaches users
  • Partner directly with Merchandising, Pricing, Finance, Operations, HR, and Legal to turn business problems into working systems, including teams new to AI tooling

REQUIRED QUALIFICATIONS

  • Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field
  • 2+ years of hands-on experience in software, data, or AI engineering
  • Strong Python and strong SQL
  • Working experience with LLM APIs, prompt design, and structured outputs
  • Demonstrated experience building a RAG pipeline end to end, including embeddings, vector search, and retrieval evaluation
  • Solid understanding of relational data modeling, joins, and query performance
  • Experience integrating systems through REST APIs and webhooks
  • Version control and basic software engineering discipline, including code review and testing
  • Clear written communication and the ability to explain technical tradeoffs to non-technical stakeholders

PREFERRED QUALIFICATIONS

  • Vector databases such as Pinecone, Weaviate, or Qdrant
  • Agent frameworks and orchestration: LangGraph, LangChain, LlamaIndex, or the Claude Agent SDK
  • Model Context Protocol (MCP) and tool design for agent access to internal systems
  • dbt for transformation, testing, documentation, and lineage
  • Cloud data platform experience — Snowflake, AWS, or GCP preferred
  • Workflow automation experience, n8n preferred (or Make, Zapier, Airflow)
  • LLM evaluation and observability tooling such as RAGAS, Langfuse, or LangSmith

 

TECHNOLOGY ENVIRONMENT

Claude Enterprise and the Anthropic API, MCP, Python, Firebird SQL, Snowflake, Apache Doris, dbt, n8n, Pinecone, Power BI, Google Workspace, on-premises Linux infrastructure, and a large purpose-built analytics warehouse spanning sales, product, customer, inventory, fleet, HR, and finance data

WHY THIS ROLE

FMP already runs Claude Enterprise across the organization, a purpose-built analytics database wired directly to AI through MCP, and a growing portfolio of production automations. This is not a research seat or an indefinite pilot — work reaches real users quickly, ownership is real, and the scope will grow with the person who fills it.

We are an EEOC/AA Employer.

An industry leader, FMP offers well-balanced compensation and benefits programs, which may include medical, dental, vision, life, 401K, profit sharing, paid holidays/vacation/sick time, STD/LTD.

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