Data Science Lead for SIOP

Carrier is the leading global provider of healthy, safe and sustainable building and cold chain solutions with a world-class, diverse workforce with business segments covering HVAC, refrigeration, and fire and security. We make modern life possible by delivering safer, smarter and more sustainable services that make a difference to people and our planet while revolutionizing industry trends. This is why we come to work every day. Join us and we can make a difference together.

About this role

As a SIOP (Sales, Inventory, and Operations Planning) Data Scientist at Carrier Corporation, this person will play a crucial role in enhancing the company's supply chain and operational efficiency. This role demands a deep understanding of data engineering and analytics, predictive modeling, and optimization techniques to drive strategic decision-making, improve inventory management, and streamline the company's overall supply chain processes. You will partner with Digital and lead a team of data scientists to work closely with cross-functional teams to ensure that Carrier Corporation remains at the forefront of supply chain excellence.

Key Responsibilities

• Utilize advanced statistical models and machine learning techniques to analyze historical data and extract actionable insights and build forecasting models related to demand and supply patterns, inventory levels, and supply chain efficiency.
• Develop and execute data-driven strategies for Sales, Inventory, and Operations Planning (SIOP) to optimize demand forecasting, inventory levels, and production schedules across Carrier, including Aftermarket Services.
• Collaborate with senior leadership to align SIOP processes with the company's strategic goals and objectives.
• Develop and refine demand forecasting models to enhance accuracy, align with business goals, and support effective inventory management.
• Apply advanced data analysis and statistical modeling techniques to extract valuable insights from internal and external data sources.
• Develop and implement predictive models for demand forecasting, inventory optimization, and production planning.
• Develop strategies and algorithms to optimize inventory levels, reducing excess and obsolete inventory while meeting customer demand.
• Create compelling data visualizations and reports to communicate key findings and recommendations to stakeholders at various levels of the organization.
• Develop and maintain key performance indicators (KPIs) to track the effectiveness of SIOP initiatives and ensure data-driven decision-making
• Oversee the execution of data-driven projects and ensure their successful delivery.
• Collaborate with cross-functional teams, including supply chain, finance, marketing, and operations, to ensure alignment with SIOP objectives.
• Collaborate with cross-functional teams to identify opportunities for improving supply chain processes, reducing lead times, and minimizing costs while ensuring product availability.
• Provide data-driven recommendations to various stakeholders to drive operational improvements.
• Work closely with sales, operations, and finance teams to align SIOP initiatives with overall business objectives and promote cross-functional collaboration.
• Identify opportunities for process improvement and implement innovative solutions to enhance SIOP performance.
• Define, track, and report key performance indicators (KPIs) related to SIOP effectiveness and make data-driven recommendations for continuous improvement.

Basic Qualifications

• Bachelor's degree
• 5+ years of experience in data science, demand forecasting, and supply chain optimization.
• 5+years of experience in data analysis tools and programming languages (e.g., Python, R, SQL).

Preferred Qualifications

• Master's degree in Data Science, Statistics, Operations Research, or a related field.

• Advanced degree (e.g., Ph.D.) in Data Science, Statistics, or a related field.
• Experience in the supply chain and manufacturing industry, with a deep understanding of its unique challenges and requirements.
• Strong understanding of statistical and machine learning techniques.
• Previous experience leading and managing a team of data scientists or analysts.
• Excellent communication and presentation skills, with the ability to translate complex data insights into actionable recommendations.
• Strong business acumen, with the ability to align data-driven initiatives with overall business objectives.
• Demonstrated expertise in building and deploying forecasting models using Amazon Forecast and Amazon SageMaker.
• Familiarity with advanced data analytics platforms and tools such as BlueYonder, O9, Kinaxis, and other industry-specific software.
• Experience working with big data and cloud computing platforms, such as AWS, Azure, or Google Cloud.
• Knowledge of data engineering principles, including data collection, cleansing, and integration techniques.
• Expertise in incorporating and analyzing external data sources to enhance demand forecasting and inventory optimization.
• Certification in data science, machine learning, or a related field.
• Proven track record of successfully implementing data-driven solutions that have had a significant impact on business operations and outcomes.
• Strong project management skills and the ability to oversee multiple initiatives concurrently.
• Continuous learning and adaptability to stay current with the latest developments in data science and supply chain optimization.

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