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Ai Data Analytics Jobs in Indiana (NOW HIRING)

This role is ideal for a seasoned data science professional with deep expertise in modeling, simulation, AI, data analysis, and programming, along with the ability to translate complex findings into ...

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that power Human Capital AI products and analytics. You will work with an AI Data Engineer (data ingestion ...

AWS Devops Cloud Engineer

Indianapolis, IN · On-site

$50.50 - $69/hr

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest ...

AWS Devops Cloud Engineer

Indianapolis, IN · Hybrid

$50.50 - $69/hr

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest ...

AWS Devops Cloud Engineer

Indianapolis, IN · On-site +1

$50.50 - $69/hr

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest ...

Senior Data Scientist

Indianapolis, IN · On-site

$120 - $160/hr

Masters in a Data Science/Analytics/AI is a plus Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities Wellness at CLA To support our CLA family members, we focus on their ...

Analyze financial & key performance indicator deal data for trends and outliers * Directly advise ... Experience leveraging AI tools to extract and visualize insights from large data sets * Experience ...

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Ai Data Analytics information

See Indiana salary details

$23

$52

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How much do ai data analytics jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for ai data analytics in Indiana is $52.10, according to ZipRecruiter salary data. Most workers in this role earn between $41.88 and $59.04 per hour, depending on experience, location, and employer.

What is the difference between Ai Data Analytics vs Data Scientist?

AspectAi Data AnalyticsData Scientist
Required CredentialsBachelor's in Data Science, Computer Science, or related fields; certifications in AI and data analyticsBachelor's or higher in Data Science, Statistics, Computer Science; advanced degrees preferred
Work EnvironmentTech companies, finance, healthcare; focus on AI-driven data analysisResearch labs, tech firms, finance; focus on data modeling and insights
Employer & Industry UsageUsed in industries leveraging AI for predictive analytics and automationUsed across industries for data modeling, predictive analytics, and research

Ai Data Analytics professionals focus on applying AI techniques to analyze data and develop automated solutions, while Data Scientists build models and interpret data to generate insights. Both roles require strong analytical skills and familiarity with data tools, but Ai Data Analytics emphasizes AI implementation, whereas Data Scientists focus on statistical modeling and research.

How does an AI data analytics professional typically collaborate with cross-functional teams within an organization?

AI Data Analytics professionals frequently work alongside departments such as marketing, operations, IT, and product development to interpret complex datasets and provide actionable insights. Collaboration often involves translating business needs into data-driven solutions, communicating findings in accessible terms, and ensuring that analytics projects align with organizational goals. Effective teamwork and clear communication are crucial, as analytics professionals must bridge the gap between technical data analysis and practical business application.

What skills and qualifications are needed to thrive as an AI data analyst?

To thrive as an AI Data Analyst, you need a strong background in statistics, data analysis, and machine learning, typically supported by a degree in computer science, mathematics, or a related field. Proficiency with tools such as Python, R, SQL, and data visualization platforms like Tableau, along with knowledge of AI frameworks such as TensorFlow or PyTorch, is essential. Strong problem-solving skills, attention to detail, and effective communication help you interpret complex data and present actionable insights to stakeholders. These skills are crucial for driving data-driven decision-making and maximizing the impact of AI initiatives within organizations.

What is AI data analytics?

AI Data Analytics refers to the use of artificial intelligence technologies to analyze and interpret large volumes of data. By leveraging machine learning algorithms, natural language processing, and other AI methods, professionals in this field can uncover patterns, make predictions, and drive data-driven decision-making. AI Data Analytics is widely used across industries to optimize operations, improve customer experiences, and gain competitive insights. The role typically involves working with big data platforms, developing models, and communicating findings to stakeholders.
What are popular job titles related to Ai Data Analytics jobs in Indiana? For Ai Data Analytics jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Ai Data Analytics jobs in Indiana look for? The top searched job categories for Ai Data Analytics jobs in Indiana are:
What cities in Indiana are hiring for Ai Data Analytics jobs? Cities in Indiana with the most Ai Data Analytics job openings:
Infographic showing various Ai Data Analytics job openings in Indiana as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $108,358 per year, or $52.1 per hour.

Vice President, Artificial Intelligence & Data

Patrick Industries

Elkhart, IN • On-site

$130 - $180/hr

Other

Re-posted 28 days ago


Patrick Industries rating

6.3

Company rating: 6.3 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

488th of 537 rated manufacturers


Job description

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Vice President, Artificial Intelligence & Data

Patrick Industries, a publicly traded company headquartered in Elkhart, Indiana, invites you to join a team of dedicated Team Members who are passionate about delivering high-quality products and exceptional customer service. As a leading solutions provider serving a diverse range of markets across the United States, our commitment to innovation, quality, and sustainability has positioned us as a high growth, diversified and empowered Team of more than 10,000! Your adventure awaits!

Patrick Industries is building its enterprise AI and data capability from the ground up — and is searching for the executive to lead it. This is a rare “zero-to-one” mandate inside a profitable, acquisitive company with 65+ years of entrepreneurial execution and 85+ operating brands: a staged, multi-year investment behind a use-case portfolio carrying more than $150M of identified value across 70+ initiatives, spanning customer-centric operations, aftermarket commerce, and back-office automation. The Vice President of AI & Data will set the operating model, formulate the AI and data investment strategy, build and scale the delivery team, own the data foundation on which it all depends, and run the engine that turns strategy into production-grade and measurable value.

The Role

Reporting to the Chief Information Officer, the Vice President of AI & Data governs, prioritizes, and delivers the enterprise AI, data, and automation initiatives that drive measurable business value across Patrick Industries. The role is the execution engine behind the enterprise AI strategy — and the steward of the data foundation beneath it — translating prioritized use cases into scalable, production-grade solutions through a DevOps-enabled, agile delivery model, and ensuring a disciplined delivery capability that is fast without being fragile.

Operating at the intersection of business and technology, the VP carries full lifecycle accountability — from intake and prioritization through build, deployment, and scaled adoption — and is expected to stay at the leading edge of a fast-moving field, continuously evaluating new models, agentic frameworks, and tools and translating them into pragmatic, well-governed advantage. The leader drives clear traceability from each use case to defined KPIs and business outcomes, strengthens the data-governance leg of the enterprise Digital Backbone, and aligns delivery to Patrick’s IT Strategic Pillars:

  • Innovative Advantage – Scale AI-, data-, and automation-driven capabilities that unlock new business value.
  • Value Optimization – Ensure measurable ROI, efficiency gains, and capital discipline.
  • Agility & Efficiency – Enable rapid, iterative delivery through modern DevOps practices.
  • Resilient Operations – Keep AI and data solutions secure, stable, and well-governed.

Areas of Responsibility

The mandate spans the operating capabilities the VP will stand up to govern, deliver, and sustain AI and data at enterprise scale.

Govern & Direct — set the agenda, control the rules, steer the portfolio

  • AI & Data Strategy & Investment — Own the enterprise AI and data strategy and roadmap, the multi-year investment plan and budget allocation, the operating model and decision rights, and an outcome thesis tied to defined value levers.
  • Data Governance, Policy, Standards & Risk — Own data governance — ownership and stewardship, quality, master data management, access, and lineage — alongside acceptable-use policy, an approved-tool catalog with exception workflow, security/model/vendor risk, and a controls library and risk register.
  • Portfolio & Program Management — Prioritize, sequence, and stage-gate the portfolio; control scope, budget, and resources; manage cadence, milestones, and dependencies; and track value realization and benefits.
  • Training, Change & Adoption — Build AI and data literacy from the executive team to the frontline, role-based training paths, change and communications plans, and a champion network that drives durable adoption.

Deliver & Run — build, run, and sustain the capabilities that produce value

  • Enterprise Data Platform & Architecture — Own the data foundation AI depends on — the lakehouse/fabric bridging 40+ ERPs, the semantic layer, master data management and entity matching, cataloging, and observability — and sequence AI delivery behind data readiness.
  • Product Ownership: LLM Platform & Utilities — Own the roadmap for shared LLMs, agents, APIs, and utilities, with monitoring, observability, evaluation, and quality controls, plus utilization analytics, financials, and vendor management.
  • Product Ownership: AI Solutions — Ensure every production solution has a named owner, a managed backlog and release plan, KPI ownership and user-feedback loops, and disciplined reuse, consolidation, and sunset decisions.
  • Technical Ownership — Set reference architecture, integration patterns, and standards; run SDLC, DevOps, and CI/CD for AI workloads; manage environments, infrastructure-as-code, and reliability (SRE); and own production support and incident response.
  • Knowledge & Content Management — Own curated knowledge bases and sources of truth, content lifecycle and access controls, retrieval infrastructure, and data-quality stewardship with ongoing SME-driven curation.

Building the Team & Delivery Engine

A central part of the mandate is to build the people and platform that make delivery repeatable. The VP will recruit and scale a dedicated team from a small founding core to roughly twenty professionals over three years — solution architecture, AI/ML and software engineering, data engineering and architecture, DevOps/MLOps, product management, and data and solution governance — operating a lean internal model that orchestrates strategic delivery partners and brand adoption rather than depending on them. The team stands up the reusable data platform, pipelines, and engineering playbooks that bend the cost curve so each successive use case is faster and cheaper than the last, while Patrick retains the architecture, intellectual property, and institutional knowledge.

Staying at the frontier of AI and data

  • Maintain an active scan of frontier models, agentic frameworks, and tooling with a disciplined evaluation pipeline that separates durable capability from hype, keeping the approved-tool catalog and reference patterns current without compromising security or governance.
  • Translate emerging capability into pragmatic roadmap and investment decisions, and continuously upskill the team so Patrick’s practice compounds rather than ages.

Traceability to the IT Strategy

Every responsibility traces to Patrick’s IT Strategic Pillars and the enterprise Digital Backbone (Architecture | Data Governance | Talent) across the Stabilize → Accelerate → Differentiate journey — and, through them, to profitable growth, operational discipline, capital stewardship, and teams built for today and tomorrow.

Strategic Pillar

How this role advances it

Innovative Advantage

Scales AI, data, and automation that expand margin, insight, and competitive differentiation, unlocking new growth across customer, aftermarket, and operations.

Value Optimization

Formulates and governs the AI and data investment for measurable ROI; enforces portfolio discipline, benefits tracking, and total-cost-of-ownership control.

Agility & Efficiency

Operates a product-centric, DevOps-enabled delivery model with a predictable cadence and rapid time-to-value.

Resilient Operations

Keeps AI and data solutions secure, reliable, and well-governed through standards, controls, SRE, and incident response.

Candidate Profile

  • Proven executive leadership in AI, data, automation, advanced analytics, or digital product delivery, with a track record of taking solutions from pilot to enterprise scale.
  • Strategic command of AI and data investment — able to shape a multi-year roadmap and budget, prioritize for ROI, and make disciplined build / buy / partner decisions.
  • Deep experience with modern data platforms and governance (lakehouse/fabric, MDM, cataloging, data quality and lineage) and the modern AI stack (LLMs and agentic systems, RAG, MLOps/LLMOps, cloud) — with the habit of staying at the frontier.
  • Strong experience operating DevOps and agile delivery at enterprise scale, with a disciplined, metrics-driven delivery capability.
  • Experience leading within federated or decentralized business environments and influencing senior business stakeholders.
  • Deep understanding of enterprise governance disciplines — security, data, architecture, and compliance — and executive communication skills suited to C-suite and Board engagement.
  • A builder who thrives in a relatively undefined, zero-to-one environment and is energized by standing up a team, a platform, and an operating model.

Executing for Results

  • Sets clear and challenging goals while committing the organization to improved performance; tenacious and accountable in driving results.
  • Comfortable with ambiguity; adapts nimbly and leads others through complex situations, taking smart, well-considered risks.
  • Viewed as having high integrity and forethought; acts transparently and consistently, always considering what is best for the organization.

Leadership

  • Leads by example, demonstrating Patrick’s principles of effective leadership: Leading for Positive Influence and culture, Leading with Humility, Embracing Responsibility, Communicating with Excellence, Leading with Accurate and Social Awareness, Building Healthy Accountability, and Servant Leadership.
  • A diplomat who promotes healthy debate toward “win-win” outcomes and inspires teams with an approachable style.
  • Thrives in a relatively undefined environment, unafraid to “roll up sleeves” across a wide range of topics, projects, and deliverables.
  • Self-reflective and open to feedback; empowers individuals and teams and drives continuous improvement.

Relationships & Influence

  • Builds strong relationships with stakeholders through emotional intelligence and clear, persuasive communication; inspires trust and followership.
  • Brings notable business understanding and developed relationships across industries and technologies.
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