- 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
- Bachelor's degree or Masters in Computer Science, Data Science, Engineering or a related field
- At least 3 years of experience in Data Science, Machine Learning or Software Engineering roles
- Proficiency in programming languages such as Python, R, Java, or Scala
- Experience with data processing frameworks such as Hadoop, Spark, or Flink
- Proficiency in natural language processing (NLP) techniques and tools (e.g., NLTK, spaCy, BERT).
- Familiarity with large language models (LLM) such as GPT-3, BERT, or XLNet
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark) is a plus
- Experience in machine learning algorithms, techniques, and libraries such as scikit-learn, TensorFlow, PyTorch or Keras
- Familiarity with data visualization tools such as Matplotlib, Seaborn, Tableau
- Experience with MLOps practices, including model deployment and monitoring, is a plus
- Knowledge of SQL for data querying and manipulation
- Understanding of version control systems like Git for collaboration and code management.Understanding of containerization and orchestration tools like Docker and Kubernetes
- Excellent analytical, problem-solving, and communication skills
Data Science Engineernew - Kiel, Deutschland - beON consult
Beschreibung
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
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.