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About This Role
We're looking for an experienced Data Scientist with a background in materials, chemistry, polymers, or chemical engineering to transform how data drives decisions across R\&D and manufacturing. You'll build advanced statistical and machine learning models, develop digital twins, and accelerate our understanding of materials, chemistry, and processes through predictive modeling and optimization. This role sits at the intersection of scientific insight, statistical rigor, and real\-world impact. The ideal candidate pairs strong full\-stack data science skills with domain intuition and thrives in translating complex technical problems into practical solutions.
As a senior individual contributor, you'll lead end\-to\-end modeling initiatives and translate insights into deployed solutions and operational recommendations that improve yield, quality, cost, and cycle time. You'll work across diverse data environments (from small, high\-value R\&D experiments to complex, high\-dimensional production datasets) turning complexity into clear, actionable direction. Your work will directly shape how teams access, use, and trust data, helping build a more agile, innovation\-focused organization.
Beyond building models, you'll help elevate our broader data science capabilities by developing reusable tools, scalable workflows, and high\-quality data assets that amplify impact across projects and teams. This is an opportunity to do meaningful, technically challenging work while shaping how data science is applied in a materials and manufacturing environment.
Key Responsibilities
Scientific and Statistical Partnership
- Partner with scientists, engineers, and manufacturing teams to frame high\-impact problems, assess data quality, and apply rigorous statistical thinking to materials, process, and production challenges.
- Bring a strong scientific lens to every analysis by ensuring methods are not only technically sound, but meaningful in the context of chemistry, materials behavior, and real\-world process dynamics.
Predictive Modeling, Digital Twins, and Optimization
- Design, build, and evolve advanced statistical and machine learning models that drive technical decision making across R\&D and manufacturing.
- Work with domain experts to support development of digital twin and hybrid models that combine first\-principles knowledge with machine learning to simulate, predict, and optimize material and process performance.
- Own models through the full lifecycle ensuring they are robust, interpretable, and actionable in operational environments.
Data Transformation and Feature Engineering
- Work across complex, multi\-source datasets spanning laboratory, pilot, and manufacturing environments, transforming raw data into structured, analysis\-ready assets.
- Engineer meaningful features that unlock insight into structure–property–process\-performance relationships and improve model performance, interpretability, and usability.
Visualization, Communication, and Decision Support
- Translate complex analyses into clear, compelling visualizations, tools, and narratives that enable teams to quickly understand and act on insights.
- Deliver recommendations that directly influence R\&D direction, process optimization, and manufacturing performance, and communicate effectively across diverse audiences.
Leadership, Capability Building \& Data Advancement
- Lead data science initiatives from problem definition through sustained use in decision\-making, working across R\&D and manufacturing.
- Act as a thought leader to technical teams by shaping analytical approaches, guiding best practices, and mentoring others in statistical thinking and disciplined use of data.
- Drive improvements in how technical data is structured, captured, and used and develop reusable tools, workflows, and codebases that scale impact beyond individual projects.
Qualifications
Education
- Bachelor’s degree in Materials Science, Chemistry, Chemical Engineering, Polymer Science, Data Science, Statistics, Computer Science, or a related technical field; Master’s or PhD strongly preferred
Experience
- 5\+ years of applying data science, statistics, or advanced analytics to complex problems in materials, chemistry, manufacturing, or related technical environments
- Track record of delivering measurable impact through modeling and analysis (e.g. improvements in yield, quality, cost, or efficiency) and owning delivery from problem framing through deployment
- Experience with modeling across data scales and structures spanning small, high\-value experimental datasets to large high\-dimensional production or process datasets
- Experience working with manufacturing, process, or production systems, and connecting analysis to real\-world operational performance
Technical Skills
- Strong proficiency in Python and modern data science tooling (e.g., pandas, NumPy, scikit\-learn, PyTorch/TensorFlow, LightGBM, SHAP); familiarity with R or JMP is a plus
- Deep grounding in statistical methods, including both frequentist and Bayesian approaches, with the ability to design experiments, quantify uncertainty, and make decisions under limited data
- Experience developing predictive and explanatory models, including feature engineering, latent variable methods, and interpretable modeling approaches
- Strong SQL skills and experience working with structured and relational data; familiarity with cloud\-based data and analytics platforms (e.g., AWS, Azure)
- Experience creating interactive dashboards or data applications to support decision making (e.g., Power BI, Tableau, Streamlit, or Plotly Dash)
- Experience building and deploying models in production or operational environments, including version control (Git), reproducibility, and lifecycle management practices
- Experience with digital twins, hybrid modeling approaches, or combining physics\-based understanding (preferred) with data\-driven techniques for prediction and optimization
- Familiarity with generative AI techniques, large language models (LLMs), or retrieval\-augmented generation (RAG) as applied to scientific or engineering workflows (preferred)
- Familiarity with materials modeling data or tools (e.g., DFT, MD, CALPHAD) or adjacent scientific computing approaches (preferred)
Who you are
- Strong communicator who can engage effectively with scientists, engineers, manufacturing teams, and leadership and translates complexity into clarity
- Comfortable operating in ambiguous, cross\-functional environments and taking ownership of high\-impact problems without waiting for direction
- Self\-directed senior IC who leads through influence by shaping analytical approaches, driving alignment across teams, and raising the bar for how data is used
- Energized by continuous learning and staying at the forefront of materials informatics, AI/ML, and scientific computing
Additional information
Applicable only to applicants applying to a position in any location with a pay disclosure requirements under state or local law:
- The compensation range that is described below is the possible base pay compensation that the company believes in good faith that it will pay for this role at the time of posting based on job grade for the position. Individual compensation within this range is based on many factors such as years of experience etc. so the company might pay more or less than the posted range and it is understood that this range may be modified in the future.
- In addition to base compensation, MonoSol provides a yearly incentive compensation bonus, a profit sharing bonus when eligible, a comprehensive benefits package including medical, dental, vision insurances, short term disability, long term disability, accidental death and dismemberment, term life insurance, voluntary term life insurance, transit flexible spending account (if applicable), employee assistance program, identity theft protection, 401k and paid time off (vacation and sick days).
Status: Full Time
Job Location: Hybrid
Compensation range \- $130,000\.00 \- $155,000\.00
Incentive Compensation Bonus Target – 10\-15%
Paid time off amount – 15 days
CLOSING
The above statements are intended to describe the general nature and level of the work being performed by employees assigned to this position. This is not intended as an exhaustive list of all responsibilities, duties, and skills required. MonoSol, LLC reserves the right to make changes to the job description whenever necessary.
Disclaimer
As part of MonoSol, LLC’s employment process, finalist candidates will be required to complete a drug test and background check prior to employment commencing. MonoSol, LLC is an equal opportunity employer. All qualified applicants will be considered without regard to race, national origin, gender, age, disability, sexual orientation, veteran status, or marital status.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $130K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MonoSol LLC., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 34% below the category median. Disclosed range: $130K to $155K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
MonoSol LLC. AI Hiring
MonoSol LLC. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $155K - $155K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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