Job Profile
The Analytics Engineer will be part of the engineering team and will be responsible for leveraging analytics in data and AI to drive development and innovation.
Vista Tech plays a vital role in the Vista group operations by delivering and accelerating comprehensive technology solutions across all brands. Vista’s end-to-end and click-to-flight solutions offer the industry's only comprehensive flight booking platform, seamlessly integrating global operations, and leveraging AI and machine learning to optimize pricing and fleet movement. Comprised of the Product Management, Engineering, and IT teams, Vista Tech’s mission is to enhance transparency and accessibility in private aviation through the development of the world's largest digital private aviation marketplace. In achieving this, Vista Tech always ensures the utmost safety and efficiency for FLIGHT CREW, EMPLOYEES and Members, while fostering a culture of innovation and excellence.
Your Responsibilities
- Technical Collaboration: Work closely among engineering teams to integrate analytics solutions into development processes, leveraging data insights to enhance engineering workflows.
- Data-Driven Engineering Culture: Spearhead the establishment of a data-driven culture within engineering teams, focusing on defining, tracking, and operationalizing engineering metrics on a feature-, product-, and company-level to drive continuous improvement.
- Mixpanel Integration and Analysis: Lead the integration of Mixpanel analytics into engineering workflows, collaborating with cross-functional teams to ensure seamless data collection and analysis.
- AI Solution Development: Plan, develop and implement AI solutions to automate tasks and associated workflows, with a focus on optimizing efficiency and reliability.
- Collaborative Problem-Solving: Collaborate with domain experts to understand engineering use cases and requirements, to develop AI-solutions.
- Model Development and Optimization: Develop, fine-tune, and optimize AI models using frameworks like TensorFlow, PyTorch, or Hugging Face, exploring various architectures and algorithms to enhance performance. Experiment with different pipelines, architectures, training algorithms, and hyperparameters to enhance model capabilities such as coherence and relevance.
- Data Transformation and Optimization: Drive the transformation of existing data structures, including unstructured data, into structures suitable for KGs and other technics of enhancing RAGs.
- Model Deployment and Monitoring: Deploy trained into production environments, ensuring robustness, scalability, and reliability. Continuously evaluate the performance of models using developed tailored metrics such as response relevance, precision, user satisfaction.
- Full-Stack Data Pipelines: Collaborate with engineering teams to develop full-stack, GPU-accelerated data pipelines for multimodal models, optimizing for efficiency and scalability.
- Algorithm Benchmarking and Optimization: Implement benchmarking, profiling, and optimization of algorithms across various system architectures, targeting LLM applications.
Required Skills, Qualifications, and Experience
- Solid understanding of statistical and mathematical concepts underlying data science and engineering methods.
- Proficiency in frameworks such as TensorFlow, PyTorch, Pandas, Jupyter, and NumPy.
- Demonstrated proficiency in utilizing Mixpanel for analytics and user tracking within engineering contexts. Ability to leverage Mixpanel's features for analyzing funnel performance, and deriving actionable insights to drive engineering decisions.
- Familiarity with cloud technologies and experience in scaling and running machine-learning solutions in cloud environments.
- Relevant experience in engineering optimization and process improvement within technical environments.
- Strong problem-solving skills and a passion for engineering innovation.
