Data Scientist - AI/ML

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Requirements:

• 3+ years of experience as a Data Scientist, with a strong focus on AI and machine learning
• Expertise in machine learning algorithms, data analysis, and statistical modeling techniques
• Proficiency in Python, R, or other data science programming languages, with experience using libraries such as TensorFlow, PyTorch, Scikit-learn, and Pandas
• Strong knowledge of deep learning, reinforcement learning, or other advanced AI techniques
• Experience with large-scale data processing, including working with big data technologies (e.g., Spark, Hadoop)
• Familiarity with cloud infrastructure (AWS, GCP, Azure) and deploying machine learning models in production
• Strong understanding of data wrangling, feature engineering, and building predictive models
• Experience with version control (Git) and working in collaborative environments
• Excellent problem-solving skills and ability to generate actionable insights from data
• Ability to communicate complex AI/ML concepts effectively to both technical and non-technical teams

Responsibilities:

• Design, implement, and deploy machine learning models to optimize software build systems, including caching, task distribution, and execution workflows
• Work with large datasets to identify patterns, anomalies, and insights that inform decisions for improving build processes and remote execution
• Develop predictive models to optimize build times, cache hit rates, and system resource utilization
• Conduct experiments to improve the efficiency of build systems through data-driven decisions, leveraging AI/ML techniques such as reinforcement learning and optimization
• Collaborate with cross-functional teams (engineering, product, and operations) to translate business problems into AI/ML-driven solutions
• Analyze customer usage data to identify opportunities for feature improvements and innovations within the NativeLink platform
• Develop custom algorithms for performance monitoring, anomaly detection, and optimization of CI/CD pipelines
• Build, test, and validate machine learning models using a variety of techniques, ensuring they are scalable, robust, and interpretable
• Build and maintain data pipelines to support model training, testing, and deployment in production environments
• Communicate findings and insights to both technical and non-technical stakeholders in a clear and actionable way