Senior Data Scientist - NQC Reduction and Manufacturing Quality

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

We are looking for a highly experienced Senior Data Scientist with a strong background in manufacturing, quality, and process optimization analytics. This role will focus on analyzing complex manufacturing and sensor data to drive measurable improvements in cost, quality, yield, and waste reduction.

• Analyze large-scale manufacturing data, including sensor, batch, recipe, and production line data
• Develop analytics solutions to identify defects, scrap, rework, and process deviations
• Perform root cause analysis and multivariate process analysis to uncover drivers of quality issues
• Build anomaly and defect detection models to proactively identify process failures
• Partner with manufacturing, quality, and operations teams to translate findings into actionable improvements
• Deliver measurable outcomes such as cost reduction, waste minimization, and quality improvement

• 10+ years of experience in applied data science or advanced analytics
• 5+ years of hands-on experience in manufacturing, quality, or process optimization analytics
• Proven experience working with manufacturing process data and quality outcome data
• Demonstrated track record of delivering measurable business impact

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.