Data Scientist

The Oracle CX Marketing team is building a new product leveraging Oracle Fusion, focusing on data management, powered by an AI based segment builder/explorer and analytical dashboards to enable marketers take advantage of single 360 view of the customer profile, transactional and behavioral data. The team is responsible for maintaining and upkeeping the data to provide the latest focused customer audience for marketers to leverage and to deliver real-time personalised content and recommendations. Combining this product with Oracle’s Marketing product suite, e.g. Responsys and Eloqua, provides the Oracle’s customer with a world class end-to-end solution for their B2C and B2B marketing needs.

**What Fires YOU up?
The Unity product and team is new, so you will have the opportunity to play a ground floor and early foundational and technical role for the team and organization.
You should come prepared to learn a lot and to help define and drive initiatives in a strong team that is helping Oracle become the #1 product organisation the world.

**Role**
- Design and Develop Data model modules derived from various domain specific data sources.
- Build, verify, tune, deploy advanced machine learning models
- Build out the GenAI platform for CX marketing use cases
- Build and deploy genAI models for Fusion Marketing
- Understand the business needs, suggest, and try new ideas to address them and pivot when required.
- Collaborate with other internal and external Analytics & Engineering teams to drive continuous improvements in metrics quality and processes Follow Oracle’s internal guidelines on customer data handling, security,privacy.

**Desired Attitude**
- Work independently and make key decisions within the scope of the role
- Enjoy working with global teams and different cultures
- Influence team members by challenging the status quo and conflict resolution
- Possess extremely strong verbal and written communication skills, including storytelling with data. Foster a collaborative team environment driven to build great software while having fun

**Prerequisites**
- MS/PhD in AI/ML/Statistics or related field emphasizing superior AI/ML skills.
- Strong knowledge of Python; Java and Scala nice to have.
- Enthusiastic self-starter with 5-7+ years industry experience as an AI/ML enginee delivering software as a service in a modern DevSecOps culture
- Knowledge of microservice architecture - Docker/Kubernetes
- Deep understanding of AI/ML domain and ability to recommend right approach to a problem.
- Cloud development experience (e.g.: AWS, OCI, etc.)
- Exposure to big data platforms, databases, and AI/ML cloud platforms.
- Ability to develop RESTful APIs to serve machine learning models in production using Flask, Django/or any other relevant tools
- Ensure production security standards are followed
- Ensure monitoring is robust and effective
- Zero downtime deployments and a high availability mindset
- Mandatory knowledge of Agile development practises.

**Nice to haves**:

- Good to have experience in differential privacy/Advanced ML/Deep Learning techniques.
- Nice to have experience in CX, Adtech or Marketing. Experience in end-to-end ownership of Machine Learning pipelines
- from data pipelines to the real time prediction engine.
- Experience in Capacity planning for AI/ML Stack and communicating the same to leadership for costing of Hardware requests
- Knowledge of Object Oriented Design principles; extra credits for working knowledge of Java
- Strong systems architecture skills
- Strong grasp of security and in managing untrusted modelling source codes
- Strong scripting skills; Linux administration and Python;
- Strong grasp of automation
- Some knowledge of system configuration tools such as Chef,Terraform GIT, Jenkins/Hudson, Artifactory, Oracle Tech Stack, e.g. Java, Weblogic, OracleDB knowledge
- Continuous Integration development/deployment

Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.