Consulting Technical Manager - Applications Developer; Clearance Required

Job Description

Oracle Government, Defense & Intelligence has an exciting opportunity for a talented Cloud Data Science Engineer with a strong background in artificial intelligence, data analytics, and cloud technologies. The ideal candidate will have hands‑on experience with cloud data‑focused technology stacks and be proficient in Python, Jupyter Lab, and modern AI methods. Successful candidate will be responsible for building, deploying, and optimizing machine learning and analytics solutions.

This position requires full time on site work (5 days/week) in the Reston VA area.

• Must possess or have the ability to obtain and maintain a TS/SCI with Poly.

• Design, build, and deploy machine learning models and analytics solutions.
• Develop and automate data‑driven pipelines, integrating end‑to‑end in‑database machine learning solutions.
• Use advanced Python programming to implement data preprocessing, modeling, and evaluation in Jupyter Lab environments.
• Collaborate with cross‑functional teams to understand business requirements and translate them into scalable AI/ML solutions.
• Optimize and monitor model performance, ensuring security and compliance with Oracle best practices.
• Contribute to the adoption of AI/ML best practices and innovative analytics strategies within the organization.
• Create thorough documentation and provide knowledge transfer sessions related to OCI‑based machine learning workflows.

• Experience working with cloud infrastructure service stacks, including Database, Compute, Object Storage, Identity and Access Management, and Key Management.
• Demonstrated expertise in using database technologies in conjunction with AI/ML and data science frameworks.
• Strong proficiency in Python for data science, including frameworks such as scikit‑learn, pandas, numpy, etc.
• Experience working in Jupyter Lab environments for collaborative AI and analytics work.
• Solid background in AI/ML algorithms and workflows.
• Experience with complex data analysis and data science tasks using SQL, including data model design.
• Excellent problem‑solving and communication skills.

• Oracle Cloud certifications (OCI Data Science, ADB, etc.).
• Experience with MLOps practices and Dev Ops in cloud environments.
• Familiarity with secure cloud architectures and enterprise compliance standards.
• Prior experience working in a global enterprise setting.

• Medical, dental, and vision insurance, including expert medical opinion
• Short term disability and long term disability
• Life insurance and AD&DD
• Supplemental life insurance (Employee/Spouse/Child)
• Health care and dependent care Flexible Spending Accounts
• Pre‑tax commuter and parking benefits
• 401(k) Savings and Investment Plan with company match
• 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 thereafter. 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.
• 11 paid holidays
• 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.
• Paid parental leave
• Adoption assistance
• Employee Stock Purchase Plan
• Financial planning and group legal
• Voluntary benefits including auto, homeowner and pet insurance

US:
Hiring Range in USD from: $97,500 - $199,500 per year. May be eligible for bonus and equity.

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.

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