Job Description

Implements machine learning (ML) models for production with minimal guidance. Contributes to the readiness of machine learning models for deployment in production. Contributes to the automation of machine learning workflows. Participates in the creation of infrastructure and frameworks to monitor the performance of machine learning models in deployment. Identifies potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Contributes to addressing issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Contributes to the development and maintenance of tools, platforms, and services for internal use. Develops low-complexity, efficient, bug-free code from scratch. Develops familiarity with current developments in the machine learning field and integrates knowledge into model development.

Responsibilities

KeyResponsibilities

MachineLearning and Data Modeling - Model Productionization:

- Utilizesmachine learning (ML) and software development knowledge to implement ML modelsfor production with minimal guidance.

- Contributesto transforming machine learning prototypes into production-ready models.

- Supportscollaboration with multiple stakeholders such as Development Leads, ProductManagement, Operations, and Release Management to make, adopt, and communicatetechnical decisions, and shape the development and delivery of software.

ModelDevelopment and Deployment - Model Deployment:

- Contributesto ML model readiness for deployment by scaling models, cleaning model code,and ensuring production quality standards are met.

- Contributesto the automation of machine learning workflows, from data extraction,transformation, and loading (ETL) to model deployment and monitoring, toestablish the continuous integration and continuous delivery of machinelearning solutions.

ModelDevelopment and Deployment - Model Performance:

- Utilizesinfrastructure and frameworks to monitor the performance and alignment withdesign criteria of trained models and/or systems.

- Monitorsthe performance of deployed models and troubleshoots independently or incollaboration with Data Science.

- Interpretsnovel metrics that provide analytical insights to non-technical stakeholders onhow well machine learning models are operating.

ModelDevelopment and Deployment - Data Quality:

- Identifiespotential issues related to data quality (e.g., bias, fairness), data security,and data privacy, and contributes to minimizing their impacts on data analysesand modeling.

- Contributesto tasks such as data cleaning, preprocessing, and feature identification toprepare for and enable model training.

InternalCollaborations and Impacts - Model Integration and Operation:

- Contributesto collaboration with multiple stakeholders (e.g., data scientists, softwaredevelopers) to integrate ML models into new or existing systems.

- Supportsthe partnership between model development and operations, ensuring smoothdeployment and continuous improvement of ML models.

- Learnsoperational considerations of model deployment (e.g., performance, scalability,stability, maintenance).

- Participatesin troubleshooting and debugging support efforts, such as addressing issues inmachine learning infrastructure and workflow, and helping to create robustsolutions to prevent future problems.

InternalCollaborations and Impacts - Tool Development:

- Contributesto the development and maintenance of tools, platforms, environments, andservices for internal use.

InternalCollaborations and Impacts - Coding and Documentation:

- Contributesto the development of efficient, bug-free, low-complexity code from scratch andproperly maintains and organizes the existing codebase.

- Adheresto best practices for version control, code review, and continuous integrationin machine learning projects.

- Updatesand maintains professional documentation for technical processes(experimentation, data collection and analyses, model building).

MachineLearning Expertise:

- Developsfamiliarity with current developments in the machine learning field andintegrates learnings into model development.

- Buildsfamiliarity with the usage and development of third-party machine learningframeworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) tocontinuously evaluate their performance and scalability, and integrate theminto production environments.

CoreResponsibilities

Planning& Execution:

- Independentlymanages work, monitoring timelines and deliverables to ensure projects orinitiatives stay on track and meet requirements.

- Proactivelyprioritizes work and adapts to resource or timeline shifts, suggestingadjustments to maintain project efficiency.

Collaboration& Partnership:

- Collaboratesacross teams to align on expectations and achieve shared objectives.

- Buildsand maintains a comprehensive understanding of business, stakeholder, and/orcustomer needs to build and support effective partnerships.

- Activelylistens to diverse perspectives and asks questions to ensure understanding ofothers.

ProblemSolving:

- Independentlyidentifies and addresses standard and non-standard issues in accordance withstandard practices, escalating more complex issues as appropriate.

- Analyzesdata and/or information from multiple sources to troubleshoot standard andnon-standard errors.

- Contributesto knowledge sharing and best practices.

ContinuousLearning:

- Embracescontinuous learning by actively seeking to build knowledge and new skillsand/or tools and staying current with industry trends and best practices.

- Seeksout and leverages feedback and training to improve skills.

- Contributesto a culture of continuous learning and knowledge sharing with team members.

ContinuousImprovement:

- Developsideas and recommends updates to increase the efficiency and effectiveness ofprocesses, protocols, and workflows within a team.

- Seeksinput from team members on alternative approaches and methods for improvingwork.

Qualifications

US: Hiring Range in USD from: $114,600 to $234,600 per annum. May be eligible for bonus, equity, and compensation deferral.

Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

Oracle US offers a comprehensive benefits package which includes the following:
1. Medical, dental, and vision insurance, including expert medical opinion
2. Short term disability and long term disability
3. Life insurance and AD&D
4. Supplemental life insurance (Employee/Spouse/Child)
5. Health care and dependent care Flexible Spending Accounts
6. Pre-tax commuter and parking benefits
7. 401(k) Savings and Investment Plan with company match
8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
9. 11 paid holidays
10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
11. Paid parental leave
12. Adoption assistance
13. Employee Stock Purchase Plan
14. Financial planning and group legal
15. Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.
Career Level - IC3