Join Argus, a leading force in automotive cybersecurity, where innovation drives our mission to safeguard drivers and manufacturers from cyber threats. As a Senior Data Scientist at Argus, you'll spearhead transformative initiatives at the forefront of automotive technology.

Why Argus?

• Pioneering the automotive cybersecurity landscape with cutting-edge solutions.
• Empowering millions of lives through revolutionary advancements in safety and security.
• Immersed in an environment of continuous learning and technological excellence.
• Opportunity to shape the future of automotive cybersecurity globally.

Responsibilities:

• Lead the development and deployment of Large Language Models (LLMs), as the primary expert, ensuring optimal performance, scalability, and practical application within the organization, setting a precedent for innovation and excellence.
• Implement advanced techniques for LLM training, including parameter efficient fine-tuning, and optimization for cost-effectiveness and response speed.
• Define and establish metrics for evaluating LLM performance, including accuracy, bias, and real-time responsiveness, driving continuous improvement and innovation.
• Develop generative AI capabilities utilizing varied big data sources to extract detailed textual and graphical insights, paving the path for pioneering data exploration and analysis.
• Architect, design, and deploy anomaly detection models for real-time monitoring of vehicle data streams, encompassing all stages from data preprocessing to model deployment in production.
• Collaborate closely with cross-functional teams to integrate data science solutions seamlessly into existing infrastructure, driving efficiency and reliability.
• Take ownership of research and development initiatives, identifying opportunities for advancement and proposing innovative solutions to address complex challenges.

Requirements:

• Bachelor’s degree in Computer Science, Electrical Engineering, or related field, with a minimum of 5 years of experience in data science.
• Proficiency in Python programming and deep understanding of machine learning and deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.
• Hands-on experience with training and fine-tuning LLMs.
• Strong background in MLOps workflows, including versioning of datasets, and experience with platforms such as MLFlow.
• Proven track record of deploying machine learning models in production environments, with a keen focus on scalability, reliability, and performance optimization.
• Expertise in anomaly detection methodologies and real-time streaming analytics, with a demonstrated ability to architect and implement end-to-end solutions.
• Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, dynamic environment and drive initiatives independently.