Data Scientist

Accentuate Staffing is working with a client that is hiring an experienced Data Scientist to work on predictive analytics and join their data and AI team. This role combines advanced machine learning research with strategic business analytics to create scalable predictive solutions that drive efficiency and smarter decision-making across the enterprise.

The ideal candidate will bring a blend of technical expertise in machine learning, cloud platforms, and data engineering, alongside strong business acumen. You’ll build and refine predictive models that help the company forecast sales, understand demand patterns, and make smarter operational decisions — from production planning to staffing and supply chain management.

Responsibilities:

• Design and implement advanced predictive and machine learning models to support sales forecasting, demand planning, and strategic decision-making.
• Build and maintain scalable ETL/ELT data pipelines that integrate structured and unstructured data from multiple business sources.
• Experiment with AI techniques such as NLP, computer vision, and generative models to explore innovative applications across the organization.
• Partner with business and IT teams to define analytics requirements, operationalize models, and integrate outputs into dashboards and reporting platforms.
• Develop and manage self-service analytics dashboards using Power BI, SAP Analytics Cloud, or similar tools to deliver actionable insights.
• Ensure data integrity, quality, and governance across predictive systems.

Requirements:

• Degree in Data Science, Computer Science, Statistics, or a related field.
• Experience in predictive analytics, data science, or AI engineering within a business setting.
• Proficiency in Python, R, SQL, and experience with cloud-based ML platforms such as Azure ML, AWS, or GCP.
• Hands-on experience with data pipeline technologies (Azure Data Factory, Spark, Hadoop) and business intelligence tools (Power BI, Tableau, or SAP Analytics Cloud).
• Strong understanding of machine learning model lifecycle management, from design through deployment and monitoring.
• Exceptional communication and stakeholder engagement skills, with the ability to translate technical work into business value.