Data Science Engineernew

Responsibilities

A Data Science Engineer typically plays a crucial role in bridging the gap between data science and engineering. Their responsibilities revolve around leveraging data science techniques and technologies to build scalable, efficient, and reliable data-driven solutions role

• Collaborate with data scientists, software engineers, and stakeholders to understand data requirements and business objectives
• Design, develop, and maintain scalable data pipelines for ingesting, processing, and analyzing large volumes of data
• Implement data preprocessing, feature engineering, and data transformation techniques to prepare data for analysis and modeling
• Build and deploy machine learning models into production environments, ensuring scalability, efficiency, and reliability
• Develop software applications, libraries, and APIs for automating data processing, analysis, and visualization tasks
• Implement machine learning algorithms using programming languages such as Python and R to develop predictive models and data-driven solutions
• Conduct text analysis, including processing unstructured data and implementing Natural Language Processing (NLP) techniques and Integrate Large Language Models (LLM) into projects
• Perform pattern analysis to identify trends and anomalies within datasets and predict future values using predictive modeling techniques
• Conduct Data Science analyses to extract insights and identify relationships within data through exploratory data analysis (EDA)
• Prepare data for analysis by cleaning, transforming, and engineering features to enhance the performance of machine learning models and improve predictive accuracy
• Demonstrate proficiency in technologies and concepts related to data science, including NLP, neural networks (NN), computer vision (CV), exploratory data analysis (EDA), supervised and unsupervised machine learning, and predictive modeling
• Implement general MLOps practices such as Continuous Integration (CI) and Continuous Deployment (CD) on local Kubernetes clusters, GPU servers, or cloud platforms like Azure AKS and Azure MLOps / Databricks
• Implement MLOps practices, including Continuous Integration (CI) and Continuous Deployment (CD), to streamline the deployment and management of machine learning models in production environments.
• Ensure code quality through intensive code reviews and support and mentor junior developers and students
• Engage in both technical and non-technical communication with stakeholders
• Manage day-to-day MLOps tasks in the Data Science and Machine Learning domain
• Contribute to the conceptualization of future applications, domains, and roadmaps for Artificial Intelligence initiatives

Qualifications

Contact Us

If we have gained your interest, we look forward to receiving your application with an up-to-date CV. We know that you are very busy and therefore do not expect a cover letter. Please use the job title “Data Science Engineer” in the subject line of your application.