Hybrid Data Science Engineer

About Us

beON consult is an exclusive IT consultancy that provides innovative IT services, powerful technology platforms and solutions to market-leading clients. We specialize in the most advanced digital projects and have a reputation for consistently providing our market-leading clients with the competitive edge to shape their future. Our company is characterized by a flat and agile structure, with an international and modern corporate culture. We have our headquarters in Kiel, Germany, and additional offices and/or training centers in Düsseldorf, Hamburg, Berlin, Frankfurt, Munich, Vienna, and Hyderabad (India). We value collaboration and our employees are the source of our success. Regardless of your location, you will be part of an engaged, diverse community and will have the opportunity to develop your full potential.

Your Benefits

If you are ready to take on responsibility, you have come to the right place and will receive above-average financial rewards, training and support. Not only will you keep up to date with the latest technology, but you will also have the opportunity to develop your individual skills. In your career with us, you can constantly expand your own area of responsibility and work creatively and independently. A continuous learning curve is guaranteed. In addition to exciting career prospects, we offer a healthy work-life balance and motivating incentives. They are embedded in an appreciative environment based on transparency, fairness and fun at work.

• Type of employment: permanent
• Workload: full-time
• Work places: India (remote)
• Languages: English
• Salary: Salary range depends on candidate’s experience
  

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

• 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
  
  

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.

We look forward to receiving your application to the following email address: careers@beon.net