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About This Role
As a Lead Data Scientist at Solstice, you will leverage deep expertise in data science to drive transformative analytics initiatives in our Integrated Supply Chain (ISC), Finance and other similar domains. You will apply advanced machine learning and statistical techniques to solve complex business problems in these areas, from demand forecasting and inventory optimization to financial modeling and anomaly detection. You will report directly to our Director of IT, AI, Analytics and Automation, and you’ll work out of our Morris Plains, NJ location, on a hybrid work schedule.
In this role, you will also serve as a bridge between technical teams and business stakeholders in Supply Chain and Finance, ensuring data\-driven insights translate into tangible business value. This position is ideal for a strategic thinker with strong applied data science skills who can combine domain knowledge with technical innovation to improve operational efficiency and financial outcomes. The role exemplifies our Solstice values and behaviors by demonstrating ownership, accountability, collaboration and a continuous improvement mindset, while fostering trust, transparency, and ethical decision making across the organization.
At Solstice, our people play a critical role in developing and assisting our employees to help them perform at their best and drive change across the company. Help build a strong, diverse team by recruiting talent, identifying and developing successors, driving retention and engagement, and fostering an inclusive culture.
KEY RESPONSIBILITIES:
Drive AI Innovation that Delivers Customer\-Focused Impact
- Lead the design and implementation of Gen AI for tailored AI predictions in the manufacturing and supply chain space.
- Spearhead development of Agentic architectures, including multi\-agent patterns, deliver innovative AI solutions.
- Identify high\-impact opportunities in demand planning, inventory management, financial forecasting, and document processing.
Continuously Improve Insights Across Key Business Domains
- Provide deep data science expertise and thought leadership in Supply Chain and Finance functions, understanding domain\-specific challenges and KPIs (e.g., forecast accuracy, working capital, cost variances). I
- Identify high\-impact analytics opportunities in areas like demand planning, inventory management, financial forecasting, and risk analysis.
Innovate Predictive \& Optimization Solutions for Growth
- Develop and deploy predictive models and optimization algorithms that address critical ISC and other domain problems.
- This includes building machine learning models for forecasting (e.g. supply and demand, cash flow), anomaly detection in financial transactions, optimization of supply chain networks, and other applied AI \& Gen AI solutions.
- Ensure models are robust, scalable, and deliver measurable improvements (e.g. increased forecast accuracy, reduced costs).
Own\-It, End\-to\-End Projects
- Lead end\-to\-end data science projects – defining concrete opportunities from vague problem statements, data extraction and exploration through model training, validation, and deployment.
- Work hands\-on with large, complex datasets (e.g., ERP data, supply chain data, financial ledgers) to extract insights.
- Maintain high standards of data quality and model performance, and implement MLOps best practices for versioning, monitoring, and continuous improvement of models in production.
Advance Together Through Strong Cross\-Functional Partnerships
- Partner closely with Supply Chain analysts, Logistics managers, Finance controllers, and IT data teams to gather requirements and implement data\-driven solutions.
- Translate complex analytical findings into actionable business insights (e.g. identifying drivers of inventory write\-offs or cost overruns) and communicating these insights to non\-technical stakeholders to inform decision\-making.
Governance, Compliance, \& Safety
- Ensure that analytics initiatives adhere to necessary compliance and governance standards. When developing models, ensure they incorporate checks to meet regulatory requirements.
- Work with functional teams to validate that AI\-driven processes maintain integrity and auditability.
People Leadership – Cultivate Teams that Innovate
- Stay abreast of the latest developments in data science, AI, and relevant industry trends.
- Proactively introduce cutting\-edge techniques (such as time\-series forecasting methods, reinforcement learning for supply chain optimization, or NLP for financial document analysis) as appropriate.
- Foster a culture of continuous improvement, where analytical methods are regularly refined and validated against business outcomes.
- Mentor junior data scientists and analysts, and promote best practices in coding, experimentation, and knowledge sharing within the analytics community.
About Solstice Advanced Materials
Solstice Advanced Materials is a leading global specialty materials company that advances science for smarter outcomes. Solstice offers high\-performance solutions that enable critical industries and applications, including refrigerants, semiconductor manufacturing, data center cooling, nuclear power, protective fibers, healthcare packaging and more. Solstice is recognized for developing next\-generation materials through some of the industry's most renowned brands such as Solstice®, Genetron®, Aclar®, Spectra®, Fluka™, and Hydranal™. Partnering with over 3,000 customers across more than 120 countries and territories and supported by a robust portfolio of over 5,700 patents, Solstice’s approximately 4,000 employees worldwide drive innovation in materials science. For more information, visit Advanced Materials .
YOU MUST HAVE
- Advanced degree (Bachelor’s or above) in Data Science, Statistics, Computer Science, Operations Research, or related field.
- 8\+ years of experience in data science or advanced analytics roles, including deploying solutions that drive measurable value (ideally in supply chain and/or finance contexts).
- Expert\-level programming in Python and SQL; proficiency with libraries/frameworks such as pandas, scikit\-learn, TensorFlow/PyTorch; experience with Databricks or Azure Cloud ML services.
- Strong grasp of statistical modeling, machine learning algorithms, and data mining techniques for time\-series forecasting and classification/regression.
- Solid understanding of supply chain (demand forecasting, S\&OP, inventory optimization) and finance (budgeting/planning, reporting, cost analysis) concepts.
- Excellent problem\-solving and ability to break ambiguous problems into actionable data questions; strong communication skills to influence business leaders.
WE VALUE
- Experience leading data science projects and/or teams; strong collaboration skills across interdisciplinary stakeholders; comfort presenting to executive audiences.
- Familiarity with enterprise data environments and tools (e.g., SAP/ERP, supply chain management systems, financial databases) and integrating solutions into production workflows (APIs, dashboards, business applications).
- Passion for innovation (competitions, publications, patents) and ability to evaluate and introduce new tools/technologies when they add value.
- Results\-driven mindset with clear objectives (e.g., reducing forecast error by X% or accelerating financial close by Y days) and tracking against outcomes.
- Commitment to continuous learning, including emerging AI trends (Generative AI and agentic AI) and responsible application in business processes.
COMPENSATION
The annual base salary range for this position is $169,316\-$211,645\. Please note that this salary information serves as a general guideline. Solstice considers various factors when extending an offer, including but not limited to the scope and responsibilities of the position, the candidate's work experience, education and training, key skills, as well as market and business considerations.
BENEFITS OF WORKING FOR SOLSTICE ADVANCED MATERIALS
In addition to a competitive salary, leading\-edge work, and developing solutions side\-by\-side with dedicated experts in their fields, Solstice Advanced Materials employees are eligible for a comprehensive benefits package. This package includes employer\-subsidized Medical, Dental, Vision, and Life Insurance; Short\-Term and Long\-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays.
*Solstice Advanced Materials is an equal opportunity employer. Qualified applicants will be considered without regard to age, race, creed, color, national origin, ancestry, marital status, affectional or sexual orientation, gender identity or expression, disability, nationality, sex, religion, or veteran status.*
Salary Context
This $169K-$211K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Solstice Advanced Materials, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $169K to $211K.
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.
Solstice Advanced Materials AI Hiring
Solstice Advanced Materials has 2 open AI roles right now. They're hiring across AI Engineering Manager, Data Scientist. Based in Morris Plains, NJ, US. Compensation range: $211K - $418K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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).
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 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.
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