About Fusemachines

Fusemachines is a 10+ year old AI company, dedicated to delivering state-of-the-art AI products and solutions to a diverse range of industries. Founded by Sameer Maskey, Ph.D., an Adjunct Associate Professor at Columbia University, our company is on a steadfast mission to democratize AI and harness the power of global AI talent from underserved communities. With a robust presence in four countries and a dedicated team of over 400 full-time employees, we are committed to fostering AI transformation journeys for businesses worldwide. At Fusemachines, we not only bridge the gap between AI advancement and its global impact but also strive to deliver the most advanced technology solutions to the world.

About the Role

This is a full-time position with hybrid working model.

Role scope

• Design and develop data science pipelines using big data tools
• Drive predictive analytics from billions of time series data points
• Provide data modeling, mining, pattern analysis, data visualization, and machine learning solutions to address customer needs
• Mentor a small team of data scientists

Requirements for the role

• Applicants should have a Master, PhD, or advanced training in applied mathematics, engineering, computer science, or a similar related field.
• 5+ years experience as a Data Scientist
• Experience working on predictive maintenance and IoT data collection
• 2+ years working with Industrial engine predictive maintenance  
• Experience with PySpark or other big data tools to create horizontally scalable solutions
• Experience in time-series and anomaly model building
• Relevant work experience, including experience applying advanced analytics, data mining, and ML models
• Excellent communication skills both written and verbal
• Hands-on experience with ML frameworks, such as PyTorch, TensorFlow, or similar
• Experience with cloud computing environments such as AWS, GCP, Azure

Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by law.