Lead Data Scientist

Job Description

As a Principal Data Scientist at Solstice, you will provide technical expertise and guidance in machine learning, statistical modeling, and data projects, translating business needs into innovative data science solutions to drive actionable insights and shape the future of our Data Science team.

Leverage deep expertise in data science to drive transformative analytics initiatives in our Integrated Supply Chain (ISC) and other similar domains. Apply advanced machine learning and statistical techniques to solve complex business problems such as demand forecasting, inventory optimization, financial modeling, and anomaly detection. Report directly to our IT Director, Analytics, Automation, and Artificial Intelligence, and work out of our Morris Plains, NJ, Houston, TX, or Charlotte, NC locations on a hybrid schedule.

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.

• 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, and 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 such as 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 communicate 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 (e.g., time‑series forecasting methods, reinforcement learning for supply chain optimization, or NLP for financial document analysis) and foster a…