Principal Engineer - Data Science

A Principal Engineer for Generative AI, focusing on Security Data Analytics, to enable Generative AI in Fraud Quality Engineering and Cyber Security Engineering activities or operations, will lead the development and implementation of AI models that detect and prevent fraudulent activities and enhance cyber security. Here's an expanded job description:

• Analyzing Security-related data to identify trends, threats, and vulnerabilities
• Developing insights to enhance security posture and inform decision making
• Identifying patterns, anomalies, and correlations to detect potential security threats
• Recommending security controls and process enhancements
• Developing predictive models to anticipate threats
• Handling security threats, and growing the dependability and reliability of our network through tools and software.
• Designing and developing generative AI models to identify and prevent fraudulent activities, such as money laundering, identity theft, and phishing attacks
• Improving model accuracy by integrating machine learning algorithms, natural language processing, and data analytics
• Collaborating with fraud experts to understand emerging fraud patterns and adapt AI models to detect new threats
• Developing and implement automated fraud detection systems that integrate with existing infrastructure
• Developing and deploying generative AI models to enhance cyber security, including threat detection, incident response, and vulnerability assessment
• Designing and implementing AI-powered security systems that can detect and respond to advanced persistent threats (APTs) and zero-day attacks
• Collaborating with security teams to integrate AI models with existing security infrastructure and processes
• Staying up-to-date with emerging cyber threats and adapt AI models to address new risks
• Leading teams of engineers and researchers to design, develop, and deploy generative AI models
• Stay current with the latest advancements in generative AI, fraud detection, and cyber security
• Guiding and mentoring junior engineers and researchers to help them grow in their roles
• Ensuring the scalability, maintainability, and efficiency of generative AI systems
• Developing and implementing evaluation metrics and testing frameworks to measure model performance and quality.

Where you'll be working

In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager.

What we're looking for...

You are good with numbers and you love to dig into data to find the story. You are detail-oriented and know how to passionate about what really matters. You are responsible for developing advanced technical solutions to provide seamless experience for the Fraud Quality Engineering team. You need to work with the Fraud QE SMEs and identify opportunities to build use-cases. Example: Build Fraud Test-cases based on the user stories and automate them , as the solution builds test-cases. You have to lead the design and implementation of the GenAI use-cases that can facilitate and reduce manual hours that can both be process oriented and also enable secured customer journey. You have to lead and transform the existing team into GenAI world and enable them to build and deploy the Fraud use cases. You should have hands-on experience with LLMs and Gen AI , particularly prompt engineering framework. You need to design, develop, test and refine AI generated text prompts to maximize the effectiveness for various applications. You should have strong analytical and problem solving skills with the ability to think critically and troubleshoot issues. You should have hands-on working with Large Language models such as Gemini AI. You should have solid understanding of cloud services, including Azure, AWS or GCP etc. You are a self-starter and a multitasker who can work independently under tight timelines. You want to make an impact by providing the data that will help improve the experience of our customers.

You will need to have:

• Bachelor's degree or four or more years of work experience.
• Six or more years of relevant work experience.
• Six or more years of experience in AI/ML / Data Science / Generative AI
• Expertise in programming languages like Python, and relevant AI frameworks
• Leadership: Proven experience leading teams and mentoring junior engineers
• Communication: Excellent communication and collaboration skills

Even better if you have one or more of the following:

• Advanced skills in programming in Python, Java, and Git using Open Source tools like Spark, Flink and Jupyter Notebook.
• Knowledge of Development Lifecycle Management, DevOps Automation methods and practices, Post-Production Model Monitoring and End-to-End Architecture.
• Ability to run workloads over multiple nodes.
• Knowledge of data administration practices and approaches for data collection and ingest using Open Source tools such as Logstash and Kafka in a Hadoop ecosystem.
• Advanced knowledge of data science concepts and applied knowledge of practices and methods within the 5 pillars of data science.
• Knowledge of statistical methods and tools like R, JMP and MiniTab.
• Knowledge of Splunk, SPL and anomaly detection and visualization development.
• Knowledge of visualization tools like Tableau.
• Knowledge of Big Data architecture, standards and standard methodologies.
• Experience combining multiple data sources to uncover correlations and causations, build models and create algorithms.
• Knowledge of the concept of managing workloads related to model processing in a Big Data ecosystem.