Senior Data Scientist

Summary

Join Gorilla Logic as a Machine Learning Engineer and become part of a dynamic team building an ML Platform on Databricks using various open-source technologies. You will design, build, and deploy scalable machine learning infrastructure and pipelines, collaborating with data scientists to optimize data pipelines and implement new models. Responsibilities include building end-to-end pipelines, identifying opportunities to apply ML, and developing production-grade models and dashboards. This role requires extensive experience in machine learning engineering, MLOps, and various programming languages and technologies.

Requirements

• 3+ years of solid hands-on Machine Learning Engineering experience with focus on MLOps
• Proven experience in building and deploying machine learning models using Python libraries like MLFlow, MLRun, scikit-learn, PyTorch, MLLib
• Programming Languages – Python (PySpark), SQL; exposure to other languages (Scala, Java, C#, JavaScript)
• Thorough understanding of programming fundamentals such as OOP, data structures, and algorithm design
• Experience with distributed compute engines (Apache Spark), cloud-based MPP databases (Snowflake, Bigquery, Redshift), and Data Lakes (Azure Data Lake, S3)
• Expertise in building software and systems that scale through a focus on MLOps
• Experience integrating Machine Learning models in production (batch, streaming and online)
• Fluent in Machine Learning algorithms
• Experience in writing data pipeline and machine learning libraries and utilities
• Industry experience building and productionizing innovative end-to-end Machine Learning systems

Responsibilities

• Design, prototype and build machine learning systems, frameworks, pipelines, libraries, utilities and tools that process data for ML tasks
• Translate data science prototypes into scalable production implementations
• Partner with data scientists to troubleshoot and optimize complex data pipelines
• Build ML Platform that can simplify implementing new models
• Build end-to-end reusable pipelines from data acquisition to model output delivery
• Identify opportunities and propose new ways to apply ML to solve challenging technical and data engineering problems and thus improve business results
• Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning, time series models and deep learning code, pipelines, and dashboards; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking implementations
• Perform code reviews to ensure architecture, code, and data standards are followed