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About This Role
*Join a high\-performing team that is going for a home run!*
Copperweld is seeking a driven and innovative AI Data Engineer / Scientist to support enterprise\-wide analytics, automation, and AI initiatives across the organization.
This position will play a key role in improving operational visibility, automating business processes, and enabling data\-driven decision\-making across finance, sales, procurement, operations, and executive leadership. The ideal candidate will combine strong technical expertise with practical business acumen and the ability to deliver high\-impact solutions in a fast\-paced manufacturing environment.
WHAT we need you to do.
- Design, build, and maintain data pipelines and integrations across ERP (PLEX), CRM, financial, and operational systems.
- Develop automated reporting and dashboard solutions to improve visibility and reduce manual reporting efforts.
- Build and deploy AI\-enabled tools for workflow automation, anomaly detection, and business analytics.
- Develop pricing and margin analysis tools to identify inconsistencies, trends, and opportunities.
- Support automation initiatives related to procurement, sales reporting, and operational KPI monitoring.
- Create standardized GPT and AI\-enabled tools for repeatable business processes and reporting functions.
- Assist in capacity modeling, forecasting, and operational planning initiatives.
- Collaborate cross\-functionally with operations, finance, commercial, and leadership teams to identify high\-ROI automation opportunities.
- Support data governance, reporting accuracy, and enterprise data consistency initiatives.
- Assist in evaluating and implementing emerging AI and analytics technologies applicable to the manufacturing environment.
- Travel to Copperweld manufacturing and operational facilities as needed to support implementation and collaboration efforts.
WHAT you need to possess.
- Bachelor’s degree in Data Science, Computer Science, Engineering, Analytics, Information Systems, or related field required.
- 5\+ years of experience in data engineering, analytics, business intelligence, or applied AI roles preferred.
- Strong SQL, data modeling, and reporting experience required.
- Experience with Python or similar programming languages preferred.
- Experience with ERP systems, reporting tools, and enterprise data environments preferred.
- Proven success implementing AI, workflow automation, data visualization platforms. and advanced analytics solutions that improved operational efficiency, reporting capabilities, decision\-making, or business performance.
- Strong analytical, problem\-solving, and organizational skills.
- Ability to communicate technical concepts effectively to non\-technical stakeholders.
- Highly collaborative, flexible, and action\-oriented with a bias for results.
- Strong business acumen with the ability to identify practical, high\-value solutions.
- Ability to thrive in a fast\-paced manufacturing environment while managing multiple priorities.
- Excellent verbal, written, and presentation skills.
Physical Requirements:
- Prolonged periods sitting at a desk and working on a computer.
- Ability to travel between Copperweld locations as needed.
- Must be able to lift up to 15 pounds at times.
WHY Copperweld?
- STRONG Business Outlook
- Comprehensive Medical, Dental and Vision Plans
- PAID Time off \& Holidays
- 401k matching
- Life and Disability Insurance
- Employer Assistance Program
*Copperweld is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity and/or expression, age, national origin, genetic information, disability, veteran status, or any other characteristic protected by federal, state or local law. Successful candidates will be required to successfully pass drug screening and background checks.*
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Copperweld, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Copperweld AI Hiring
Copperweld has 1 open AI role right now. They're hiring across Data Scientist. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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