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About This Role
Accelerate the possible by joining a winning Amcor team that’s transforming the packaging industry and improving lives around the world.
At Amcor, we unpack possibility through our innovative and responsible packaging to provide solutions that benefit our customers, our people and our planet. More than 10,000 consumers worldwide encounter our products every second and rely on us for safe access to food, medicine and other goods. We value their trust by making safety our guiding principle. It’s our core value and integral to how we do business.
Beyond this core principle, our shared values and behaviors unite us as we work together to elevate customers, shape lives and protect the future. We champion our customers and help them succeed. We play to win – adapting quickly in an everchanging world – and make smart choices to safeguard our business, our communities and the people we serve for generations to come. And we invest in our world-class team, empowering our colleagues to unpack their potential, because we believe when our people grow, so does our business.
To learn more about playing for Team Amcor, visit www.amcor.com I LinkedIn I Glassdoor I Facebook I YouTube
Job Description
As a Sr. Data Scientist in Amcor Core R&D, you'll lead the development of models and advanced analytics tools, enabling data-driven decision making across the global Flexibles R&D organization. This role develops mathematical optimization, machine learning, and AI solutions, and contributes to the data pipelines that feed them — to support cost reduction, manufacturing efficiency, and product design.
This position works closely with cross-functional teams to integrate enterprise data into the R&D development process and enable scalable, high-impact capabilities that drive business results. Serves as a senior technical expert for optimization, AI, and advanced analytics, providing recommendations that help shape the R&D analytics roadmap and digital transformation initiatives.
Optimization, AI, & Advanced Analytics
- Champion the digital transformation of R&D, scaling advanced analytics, optimization, AI, and automation solutions
- Build optimization models that minimize material cost across product structures and recipes while meeting performance requirements
- Develop scheduling and process optimization models — sequencing for transition waste minimization and improving how products are matched to asset capabilities
- Develop and deploy analytics tools and predictive models that improve the speed, throughput, and robustness of the R&D design and development process — including early detection of design risks and material property prediction
- Translate complex analytical insights into clear, actionable recommendations for technical and non-technical audiences
- Continuously improve modeling approaches, optimization techniques, and analytics tools through adoption of best practices and emerging methods
- Act as a steward for proper use and protection of confidential R&D data
Systems & Business Intelligence
- Serve as subject-matter-expert in Amcor R&D systems and data structures
- Use knowledge of product structures, R&D design process and manufacturing processes to identify opportunities where advanced analytics tools can create value for Amcor
- Design and implement data structures and features that enable development of advanced analytic tools and predictive models
- Perform data modeling and feature engineering to support optimization and analytics tools, ensuring data structures are fit for advanced modeling and scalability
- Partner closely with data engineers to ensure robust data pipelines and high-quality, connected data sources across systems
Cross-Functional Collaboration
- Act as a strategic partner to R&D, Operations, Procurement, Finance, Commercial, and IT to understand business challenges and propose solutions that connect to Advanced Analytics team strategy
- Collaborate with Business Intelligence teams and systems owners to align analytics tools with enterprise data standards and reporting frameworks
- Collaborate cross-functionally to understand business challenges that can be solved via advanced analytics techniques and advise on data quality and structure required to support capability
- Contribute as a senior technical consultant to solve complex business and technical challenges using data and advanced modeling techniques
What Success Looks Like
- Scaled deployment of enterprise analytics solutions
- Increased speed and quality of R&D decision-making
- Strong adoption of AI and advanced analytics tools across the organization
- Robust, trusted data infrastructure supporting innovation and growth
Qualifications/Requirements
- MS or PhD in Data Science, Operations Research, Engineering, Applied Mathematics, or related field.
- 5+ years (3+ with PhD) of industry experience in operations research, data science, and/or technical engineering field.
- Demonstrated success improving organizational performance through analytics, AI/ML models.
- Proven experience managing cross‑functional projects with measurable impact.
- Proven statistical modeling and machine learning skills; expertise in mathematical optimization is essential.
- High Proficiency in Python, SQL, including building or contributing to data pipelines.
- Experience with Azure cloud platform preferred. Experience with optimization solvers (HiGHS, Gurobi, CPLEX)
- Proven understanding of polymer science, film extrusion, or food-packaging interactions is highly preferred.
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Our Expectations
We expect our people to be guided by The Amcor Way and demonstrate our Values every day to enable the business to win. We are winning when:
- Our people are engaged and developing as part of a high-performing Amcor team
- Our customers grow and prosper from Amcor’s quality, service, and innovation
- Our investors benefit from Amcor’s consistent growth and superior returns
- The environment is better off because of Amcor’s leadership and products
Equal Opportunity Employer/Minorities/Females/Disabled/Veterans/Sexual Orientation/Gender Identity
Amcor is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
If you would like more information about your EEO rights as an applicant under the law, please click on the "Know Your Rights: Workplace Discrimination is Illegal" Poster. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the employment process, please call 224-313-7000 and let us know the nature of your request and your contact information.
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Benefits
When you join Amcor, you will have access to a comprehensive benefits and compensation package that includes:
- Medical, dental and vision plans
- Flexible time off, starting at 80 hours paid time per year for full-time salaried employees
- Company-paid holidays starting at 8 days per year and may vary by location
- Wellbeing program & Employee Assistance Program
- Health Savings Account/Flexible Spending Account
- Life insurance, AD&D, short-term & long-term disability, and voluntary benefits
- Paid Parental Leave
- Retirement Savings Plan with company match
- Tuition Reimbursement (dependent upon approval)
- Discretionary annual bonus program (initial eligibility dependent upon hire date)
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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Amcor, 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 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Amcor AI Hiring
Amcor has 1 open AI role right now. They're hiring across Data Scientist. Based in Neenah, WI, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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