Principal Data Scientist

Solstice Advanced Materials • Morris Plains, NJ

1d • Hybrid

About The Position

As a Principal Data Scientist at Solstice, you will leverage deep expertise in
data science to drive transformative analytics initiatives in our Integrated
Supply Chain (ISC) 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 IT Director, Analytics,
Automation, and Artificial Intelligence, and you’ll work out of our Morris
Plains, NJ, Houston, TX or Charlotte, NC 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.

Responsibilities

• 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.
• 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). Identify high-impact analytics opportunities in areas like demand planning, inventory management, financial forecasting, and risk analysis.
• 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).
• 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.
• 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.
• 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.
• 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 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.entifying and developing successors, driving retention and engagement, and fostering an inclusive culture.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed