Interested in this Data Scientist role at Armstrong World Industries?
Apply Now →Skills & Technologies
About This Role
Primary location: Lancaster, Pennsylvania
Relocation offered: No
Employment status: Full-Time
Travel: <10%
Non-compete: No
The estimated base salary range for this role is $121,000 to $160,000 per year.
Individual pay is based upon location, skills and expertise, experience and other relevant factors
What does it mean to work at Armstrong?
It means being immersed in a supportive culture that recognizes you as a key player in Armstrong's future. We are a large company with a local feel, where you will get to know and collaborate with leadership and your colleagues across the company.
By joining us, you'll have the opportunity to make the most of your potential. Alongside a competitive remuneration package, you will receive:
- A benefits package including: medical, dental, prescription drug, life insurance, 401k match, long-term disability coverage, vacation and sick time, product discount programs and many more.
- Personal development to grow your career with us based on your strengths and interests.
- A casual work environment where we have a recognition program for our team, and service awards. You will also be able to make the most of our fitness center and lunchtime cafe.
A working culture that balances individual achievement with teamwork and collaboration. We draw on each other's strengths and allow for different work styles to build engagement and satisfaction to deliver results. *
Armstrong is seeking a Senior Data Scientist to lead the development of advanced analytics, machine learning models, and data-driven solutions that power key business decisions. This role combines deep technical expertise with strategic thinking and strong communication skills to translate complex data insights into clear business value.
As a Senior Data Scientist, you will partner closely with cross-functional teams across data engineering, ML Ops engineering, product, pricing, finance, operations, and strategy to design, develop, and deploy scalable models and analytical tools that drive measurable outcomes.
What You'll Do
- Lead the design, development, and deployment of machine learning and AI models to solve complex business challenges.
- Partner with stakeholders to identify high-impact use cases and define measurable success criteria.
- Identify and build the right model to enable the business outcomes desired for each use case.
- Analyze large, complex datasets to uncover actionable insights and support data-driven decision-making.
- Collaborate with data engineers to ensure robust data pipelines and model integration into production systems.
- Build prototypes, conduct experiments, and iterate on model performance to drive continuous improvement.
- Connect LLMs to data sources to automatically summarize data, identify key metrics, and answer natural language questions.
- Leverage LLMs to generate summaries of analysis results and create visualizations.
- Apply LLM-powered NLP techniques for tasks such as text classification, sentiment analysis, and named entity recognition.
- Document processes and best practices in data science methodologies, coding standards, and model evaluation.
- Communicate results and recommendations clearly to both technical and non-technical audiences using data visualization and storytelling.
- Stay current with emerging tools, techniques, and trends in AI, ML, and data science.
What will make you successful
- Strong proficiency in Python and key ML/data libraries (Databricks, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.).
- Experience using AI platforms (Databricks).
- Expertise in statistical modeling, predictive analytics, and feature engineering.
- Experience with cloud platforms (AWS, Azure, or GCP) and MLOps practices.
- Proficiency in SQL, R, & Python and experience working with large-scale data environments.
- Strong understanding of model evaluation, A/B testing, and business impact measurement.
- Excellent communication skills with the ability to simplify complex topics for business audiences.
- Experience with NLP, LLMs, and generative AI models.
- Familiarity with data visualization tools (Tableau, Power BI, Plotly, etc.).
- Background in deploying models into production or integrating with enterprise applications.
- Experience working in Agile environments and cross-functional teams
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (PhD preferred).
- 5+ years of professional experience in data science, machine learning, or applied analytics.
Why should you join Armstrong World Industries?
Armstrong World Industries (AWI) is an Americas leader in the design and manufacture of innovative interior and exterior architectural applications including ceilings, specialty walls and exterior metal solutions. With approximately $1.4 billion in revenue, AWI has about 3,700 employees and a manufacturing network of 21 facilities in North America.
At home, at work, in healthcare facilities, classrooms, stores, or restaurants, we offer interior solutions that help to enhance comfort, save time, improve building efficiency and overall performance, and create beautiful spaces.
For more than 160 years, we have built our business on trust and integrity. It set us apart then, and it sets us apart now, along with our ability to collaborate with and innovate for the people we're here to serve - our customers, our shareholders, our communities and our employees.
We are committed to developing new and sustainable architectural solutions, with design and performance possibilities that make a positive difference in spaces where we live, work, learn, heal and play. It's an exciting, rewarding business to be in, and we're committed to continue to grow and prosper for the benefit of all of our stakeholders. We hope you join us.
Our Sustainability Ambition
"Bringing our Purpose to Life" - lead a transformation in the design and building of spaces fit for today and tomorrow.
We are committed to:
- Engaging a diverse, purpose-driven workforce;
- Transforming buildings from structures that shelter into structures that serve and preserve the health and well-being of people and planet;
- Pursuing sustainable, innovative solutions for spaces where we live, work, learn heal and play;
- Being a catalyst for change with all of our stakeholders; and
- Making a positive difference in the environments and communities we impact.
About the location (Lancaster PA)
Lancaster, PA. A great central location in South Central Pennsylvania, Lancaster is ideally situated for easy access to major metropolitan cities such as Philadelphia, Baltimore, Washington DC, and New York City. Lancaster offers a vibrant arts and entertainment community with wonderful historic sites, B&Bs, museums, great shopping, entertainment venues and restaurants.
Armstrong is committed to engaging a diverse, purpose-driven workforce. As part of our dedication to diversity, AWI is committed to Equal Employment Opportunity and all qualified applicants receive consideration for employment without regard for race, sex, color, national origin, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at by email at AWI talent acquisition and let us know the nature of your request and your contact information. Requests for accommodation will be evaluated on a case-by-case basis. Please note that only inquiries concerning a request for reasonable accommodation will be responded to from this email address.
Come and build your future with us and apply today!
Salary Context
This $121K-$160K range is below the median for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Armstrong World Industries, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($140K) sits 26% below the category median. Disclosed range: $121K to $160K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Armstrong World Industries AI Hiring
Armstrong World Industries has 1 open AI role right now. They're hiring across Data Scientist. Based in Lancaster, PA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.