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About This Role
Hunter Douglas is the world's leading manufacturer of window coverings and a major manufacturer of architectural products. We are a brand that you know and trust. With more than 100 years of innovation, we've defined our industry with proprietary products that deliver revolutionary style and functionality and can be found in millions of homes and commercial buildings globally.
We are searching for candidates that are driven, intelligent, creative, and entrepreneurial. By offering challenging and accelerated opportunities for growth, powered by a shared hunger for success, we create a space for your career to thrive. In return for your expertise, we are committed to providing competitive and robust total compensation and benefit packages to ensure you feel valued. Our dream is to become the fastest growing, most loved, window covering company in the world. *What's yours?*
Position Overview
Hunter Douglas is seeking a motivated and experienced Data Scientist to join our growing AI Analytics \& Digital Transformation team. This individual will play a key role in advancing Hunter Douglas's digital and data\-driven transformation by leveraging AI, machine learning, and advanced analytics to optimize operations, enhance customer experiences, and support strategic decision\-making across our global business.
The successful candidate will partner with business leaders across supply chain, logistics, manufacturing, sales, and customer experience to identify opportunities where AI and data science can drive measurable value — from predictive demand modeling and inventory optimization to intelligent automation and sustainability initiatives.
What you'll do
- Design, develop, and deploy advanced AI/ML models and algorithms that improve operational efficiency, forecasting accuracy, and decision intelligence across the Hunter Douglas supply chain.
- Conduct data exploration, feature engineering, and model tuning to ensure accuracy, robustness, and business applicability.
- Collaborate with business stakeholders to translate complex challenges into data\-driven solutions aligned with organizational goals.
- Monitor, retrain, and optimize models post\-deployment to maintain high performance as data and business conditions evolve.
- Apply state\-of\-the\-art methods such as neural networks, reinforcement learning, natural language processing (NLP), and generative AI to tackle diverse business challenges.
- Contribute to the AI strategy roadmap, ensuring alignment with Hunter Douglas's digital transformation initiatives.
- Work with data engineers and IT to ensure efficient data pipelines, scalable infrastructure, and model deployment.
- Partner with internal and external technical teams, vendors, and consultants to implement and scale AI solutions.
- Communicate findings and insights clearly to both technical and non\-technical stakeholders through visualizations, presentations, and storytelling.
- Stay at the forefront of AI and analytics innovation, evaluating new tools, methodologies, and technologies for potential adoption.
- Collaborate with digital product and IT teams to develop and integrate AI\-assisted tools and automations within Hunter Douglas's VIBE platform and data ecosystem.
- Experiment with AI coding assistants (e.g., Cursor, GitHub Copilot, OpenAI Codex) to accelerate analytics and model development and improve reproducibility.
- Support the creation of custom AI copilots and prompt libraries for use in digital supply chain tools, analytics dashboards, and planning applications.
- Partner with the Digital Supply Chain Leader to prototype intelligent process automation for recurring planning, logistics, quality, or operational workflows.
Who you are
- Bachelor's degree in a quantitative field (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics, Operations Research, or related).
- 5\+ years of experience in AI/ML model development, deployment, and performance optimization in a business or industrial context.
- Strong proficiency in Python, R, or SQL for data science and model development.
- Demonstrated experience with machine learning frameworks and cloud platforms (AWS, Azure, GCP).
- Proven ability to design and operationalize data science solutions in real\-world environments.
- Experience with predictive modeling, optimization, and time\-series forecasting, ideally within supply chain, operations, or commercial analytics.
- Knowledge of data visualization and BI tools (Power BI, Tableau, or similar).
- Strong problem\-solving, analytical, and communication skills.
- Familiarity with version control (Git) and collaborative development practices.
Preferred Education, Experience, and Skills:
- Master's in Data Science, Engineering, Statistics, or related quantitative discipline.
- Experience working in manufacturing, supply chain, or consumer goods industries.
- Knowledge of IoT data analytics, predictive maintenance, or computer vision in industrial applications.
- Understanding of AI frameworks and automation pipelines for large\-scale AI deployment.
- Proven ability to manage multiple projects and deliver business impact through analytics.
- Strong collaboration and communication skills to engage with diverse business teams.
- Experience working with ERP and supply chain systems (SAP, Kinaxis, Oracle, etc.).
Travel:
- Travel up to 20% may be required for project collaboration and site visits
What's in it for you
- Annual base salary range: $95000\-$125000
- Bonus target range: 20%\-25%
- Generous benefits package including medical, dental, vision, life, disability
- A company culture that prioritizes internal development and professional growth
- Time off with pay
- 401(k) plan with a degree of employer matching
- Paid parental leave
- Wellness programs and product discounts
*Please note, all offers presented to candidates are carefully crafted to ensure market competitiveness, equity, and reflect the individual candidate's education, experience, skills and potential.*
*Hunter Douglas is an Equal Opportunity Employer and complies with applicable employment laws. EOE/M/F/Vet/Disabled are encouraged to apply.*
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Salary Context
This $95K-$125K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Hunter Douglas, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($110K) sits 46% below the category median. Disclosed range: $95K to $125K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Hunter Douglas AI Hiring
Hunter Douglas has 1 open AI role right now. They're hiring across Data Scientist. Based in Broomfield, CO, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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