Lead Data scientist

What's the role?:

• MLOps Strategy Development: Design and implement MLOps frameworks and best practices to facilitate the seamless deployment and monitoring of AI/ML models, ensuring high performance and reliability.
• CI/CD for ML Pipelines: Build and maintain continuous integration and continuous deployment (CI/CD) pipelines tailored for machine learning applications, ensuring smooth transitions from development to production.
• Model Performance Monitoring: Establish metrics and monitoring systems to track model performance, data drift, and other key indicators, enabling proactive maintenance and optimization.
• Infrastructure Management: Collaborate with data engineers and IT teams to design and manage cloud infrastructure (AWS, GCP, Azure) that supports scalable machine learning workloads.
• Version Control for Models: Implement version control practices for ML models and datasets, ensuring reproducibility and traceability of experiments.
• Collaboration and Communication: Work closely with data scientists and software engineers to facilitate collaboration, ensuring that models are production-ready and aligned with business requirements.
• Documentation and Knowledge Sharing: Create comprehensive documentation for MLOps processes, workflows, and guidelines. Foster a culture of knowledge sharing within the team to enhance collective expertise.
• Innovation in MLOps: Stay updated on industry trends and emerging technologies in MLOps, integrating new tools and methodologies that enhance operational efficiency.
  Research and Development: Contribute to the development of innovative MLOps solutions and frameworks, focusing on optimizing performance and reliability.
• KPI Definition: Work with management to identify and define key performance indicators (KPIs) to assess the effectiveness of MLOps initiatives and model impact.
  
  
  

• Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
• 7+ years of experience in MLOps or related roles, with a strong focus on deploying and managing machine learning models in production environments.
• Proficiency in tools and frameworks for MLOps, such as MLflow, Kubeflow, or similar platforms.
• Experience with cloud computing platforms (AWS, GCP, Azure) and their ML services.
• Strong programming skills in languages such as Python, along with familiarity with tools like Docker and Kubernetes for containerization and orchestration.
• Understanding of machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch) to effectively support data scientists.
• Excellent problem-solving skills with a focus on operational efficiency and scalability.
• Strong communication skills, with the ability to collaborate effectively across teams.

Join HERE as we push the boundaries of MLOps and machine learning, contributing to innovative solutions that shape the future. If you are passionate about optimizing ML workflows and enhancing operational performance, we encourage you to apply!

HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.

At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.