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What does a Data Scientist do?

A Data Scientist, often referred to as Research Scientist, is responsible for utilizing advanced statistical techniques to automate business processes and enhance customer-facing products.

Data Scientists develop and fine-tune machine learning models for tasks spanning across anomaly detection, customer churn prediction, personalised product recommendations, detecting tumors in X-ray images, chatbots and others.  Working closely with Software Engineers or ML Engineers, they ensure the seamless deployment and monitoring of their models, contributing to the company’s data-driven decision-making process and technological advancements.

Common skills for Data Scientists include proficiency in Python, Spark, SQL, Pandas, NumPy, Scikit-Learn, TensorFlow, Keras, PyTorch, as well as expertise in data visualization with Matplotlib, Seaborn, and Plotly.

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What does a Machine Learning Engineer do?

A Machine Learning Engineer, in contrast to a Data Scientist, doesn't necessarily delve into the same depth of predictive models' intricacies and underlying mathematics. Rather, their expertise revolves around mastering the software tools crucial for practical model application and usability. Machine Learning Engineers, also known as Applied Scientists, design efficient algorithms for automation solutions, focusing on deploying and managing high-performance models using ML Ops techniques. The responsibilities of Machine Learning Engineers encompass building and deploying machine learning models into production environments, optimizing existing models, and staying abreast of the latest advancements in machine learning algorithms and technologies.

Machine Learning Engineer skills include a knowledge of Python and Spark, Scikit-Learn, TensorFlow, Keras, PyTorch alongside tools like MLFlow, DVC, TensorFlow Serving or Bento ML, Evidently AI or Fiddler for robust model monitoring.

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What are major responsibilities of Data Engineers, Data Analysts, Data Scientists, and Machine Learning Engineers?

The radar chart below illustrates the shared responsibilities across Data Engineers, Data Analysts, Data Scientists, and Machine Learning Engineers (source).

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How much does a Data Engineer make per year in Germany?

According to leading employment sites such as  Kununu (German version of Glassdoor), Glassdoor as well as TechPays.eu, in 2023 Data Engineers, depending on the experience level and the skills, can expect to earn anywhere between 59'000 and 88'000+ Euro per year: 

6

How much does a Data Analyst make per year in Germany?

Data analysts often earn less than other data-related roles due to factors such as their narrower scope of responsibilities, which primarily involves data interpretation and visualisation rather than complex statistical modelling or algorithm implementation. Data Analyst salary range starts at 51'000 Euro and goes up and beyond 76'000 Euro.

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How much does a Data Scientist make per year in Germany?

If you’ve read this far, you probably already realise that Data Scientist salary is high enough due to cool and complex intellectual challenges they solve on the job. The annual salary range for a Data Scientist starts at 59'000 Euro and goes over 88'000 Euro, which is similar to what Data Engineers make.

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How much does an ML Engineer make per year in Germany?

Machine Learning Engineers salaries tend to be higher vs Data Scientist salaries and other Data Science roles. ML Engineers role entails a strong emphasis on designing efficient algorithms, deploying and managing high-performance models, which is complicated and super valued in business. ML Engineers starting annual salary is 61'000 Euro and can go up to 90'000 Euro and more.

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What are the main take aways?

Despite some overlapping skills, each role contributes distinct expertise to the data ecosystem. Data Analysts focus on data interpretation for businesses. Data Scientists and Machine Learning Engineers concentrate on developing advanced models earning companies more money and solving customers problems. And Data Engineers lay the foundation for data processes, while handling data governance and infrastructure. This diversity enables data teams to operate and solve business problems efficiently. Career trajectories within these roles often involve advancing to more specialized positions or transitioning into related domains such as product management or business development.

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Thank you for reading. If you found this useful, please, share it with your friends. Also we run a page on Data Job with the salary ranges for Data Scientists and soon Data Engineers, too.

Alex P

P.S. Looking for a data science job in Germany at a tech company with interesting challenges, pay and work culture? Check our curated list of data science jobs in Germany.