Principal Applied Scientist

Job Description

Oracle Corporations SaaS Engineering team is setting up an exciting new team to work on advances in service reliability with teams of autonomous AI agents. This initiative aims to develop a robust system using advanced ML/AI tools to analyze system logs, predict failures, and autonomously resolve issues before they impact cloud services. The project combines the cutting-edge domains of anomaly detection and autonomous agents to enhance service resiliency. This newly formed team will be crucial in driving innovation and ensuring the reliability of Oracles cloud services, making significant contributions to service uptime and customer satisfaction.

Requirements:

● Extensive Experience: 6 years of experience in data science, with a strong focus on machine learning and predictive modeling. Demonstrated ability to lead complex data science projects from conception to deployment.

● Technical Expertise: Strong expertise in Python and R programming languages, with a deep understanding of machine learning algorithms such as LSTM, GRU, and Transformers. Proficiency in using libraries and frameworks like TensorFlow, PyTorch, and scikit-learn.

● Big Data Proficiency: Proven track record of working with large datasets and big data technologies, including Hadoop and Spark. Experience in handling and processing large-scale time-series data.

● Leadership Skills: Excellent leadership skills with a proven ability to mentor and guide junior team members. Strong communication skills to effectively collaborate with cross-functional teams and present complex technical information to non-technical stakeholders.

—– Problem-Solving Ability: Strong analytical and problem-solving skills, with the ability to identify and address potential issues proactively. Experience in feature engineering, model tuning, and performance optimization.

● Educational Background: Advanced degree (Ph.D. or Masters) in Computer Science, Data Science, Statistics, or a related field. Relevant certifications in machine learning or data science are a plus

Career Level – IC4

Responsibilities

Responsibilities:

● Lead ML Model Development: Spearhead the development and implementation of sophisticated machine learning models specifically designed for anomaly detection in system logs. This includes selecting appropriate algorithms, designing model architecture, and ensuring robust model training and validation.

● Data Analysis & Prediction: Conduct in-depth analysis of large-scale, time-series data to identify patterns and anomalies that can predict service failures. Utilize statistical methods and machine learning techniques to develop predictive models that can preemptively address potential issues.

● Mentorship & Collaboration: Mentor junior data scientists, providing guidance on best practices in data science, model development, and problem-solving techniques. Foster a collaborative environment by working closely with cross-functional teams, including software engineers, product managers, and operations staff, to align on project goals and deliverables.

● Innovation & Optimization: Drive continuous innovation in feature engineering, model optimization, and the application of new technologies to enhance the accuracy and performance of predictive models. Stay updated with the latest advancements in machine learning and artificial intelligence to integrate new methodologies into the project.

● Reporting & Documentation: Ensure thorough documentation of model development processes, data analysis methodologies, and project outcomes. Regularly report on project progress, challenges, and achievements to senior leadership and stakeholders.

Minimum Qualifications: PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning Techniques OR Masters or Bachelors in one or more of these fields. Minimum 4 years work experience in the areas of machine learning, computer vision, natural language processing or data mining with a PhD OR 5 years experience with a Master’s or Bachelors. Preferred Qualifications: Scientific thinking and the ability to invent, with a track record of contributing to the advancement of the field. Demonstrated experience in successfully designing and shipping models using machine learning, deep learning, and statistical modeling across different data domains and modalities. Experience in optimization and scaling of ML solutions for real world business use cases. Training machine learning models with large scale data using techniques such as data and model parallels. Experience in technical leadership of data science groups/projects. Track record of innovation in creating novel algorithms and advancing state of the art solutions. Demonstrated experience in engaging and influencing business leaders in solution path design. Publications at top-tier peer-reviewed conferences or journals. Hands on experience in applicable programming languages in a production service environment. Depending on the job there may be additional minimum requirements and/or preferred qualifications.

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