With the growing importance of data in corporate strategies, the data scientist has become a key player. Their expertise enables them to leverage Big Data to tackle strategic challenges, whether these involve understanding markets, optimising performance or forecasting trends. What is their role, what tasks are entrusted to them and why is this profession so sought-after? Here’s a comprehensive overview of this fast-growing profession.
Overview
What is a Data Scientist?
Data scientists are experts at data analysis and exploitation, capable of transforming complex data sets into decision-making tools. Combining technical skills and good business acumen, they meet companies’ needs by providing them with concrete solutions. Whether it’s with a view to anticipating trends or improving performance, they provide actionable insights that help to guide strategic choices.
What is the data scientist's role within a company?
In business, the data scientist occupies a central position, linking data to strategic decisions. They intervene wherever data can create value. This can range from analysing customer behaviour to optimising internal processes or anticipating future events.
Working with departments such as marketing, finance and R&D, the data scientist transforms raw data into actionable insights. They also design predictive models in order to solve complex problems, thereby contributing directly to the company’s competitiveness.
What does a data scientist do?
Data collection and preparation
The data scientist identifies, collects and cleans up this information, to ensure it is usable. If reliable models are to be built, a solid foundation is essential.
Exploratory Data Analysis (EDA)
Exploratory analysis can detect trends, correlations or anomalies in the data, providing a clear vision of its potential.
Building predictive models
Using advanced algorithms, the data scientist creates models capable of predicting future results, such as sales or customer behaviour.
Reporting results
The data scientist simplifies and illustrates his discoveries so that all stakeholders can benefit from them, even if they lack technical expertise.
How do I become a data scientist?
In a field as competitive as data science, training plays an essential role, helping you acquire the necessary skills and stand out from the crowd.
Higher education
Most data scientists start out with a Master’s or a PhD in fields such as mathematics, statistics, computer science or data science. Swiss universities, such as EPFL (École polytechnique fédérale de Lausanne) and ETH Zurich, offer highly sought-after specialised courses.
Professional certifications
Recognised certifications such as Google Professional Data Engineer, Microsoft Azure Data Scientist Associate or IBM Data Science Professional Certificate enable you to enhance your skills and obtain the specific know-how that is highly prized by recruiters.
Further training and bootcamps
For those looking to quickly master the tools and techniques of data science, continuing education courses and bootcamps offer a practical alternative. They are particularly useful for professionals transitioning between careers or wanting to hone their skills. The programmes offered by recognised players such as DataCamp, Le Wagon and Ironhack stand out due to their hands-on approach. Lasting anything from a few weeks to several months, these intensive training courses are based on concrete case studies and real-life projects, providing learners with preparation tailored to meet the demands of the data scientist’s job.
What qualities does a data scientist need?
Being a data scientist requires solid technical expertise and some fundamental personal skills, to meet the many challenges posed by data management and analysis.
Hard skills
- Data analysis: the ability to process, clean and interpret data, to extract relevant insights.
- Programming: the ability to develop automated solutions and advanced analysis models.
- Mathematics and statistics: master fundamental concepts in order to design reliable models.
- Machine learning: creating and optimising algorithms to solve complex problems.
- Databases: managing and querying systems to structure and exploit information.
- Data visualisation: clear, concise presentation of analysis results, to facilitate decision-making.
- Technological adaptability: ability to incorporate new tools and methods quickly.
Soft skills
- Curiosity: a keen interest in innovations and emerging approaches.
- Synthesis: the ability to simplify complex problems, to make them more comprehensible.
- Rigour: attention to detail and reliability in your analysis.
- Pedagogy: the ability to convey technical concepts to a non-specialist audience.
- Flexibility: the ability to adapt to changing environments.
- Team spirit: effective collaboration with a wide range of professionals.
- Integrity: compliance with ethical and confidentiality standards.
How much do data scientists earn?
In Switzerland, salaries for data scientists are among the most attractive out there. A beginner can expect to earn between CHF 90,000 and 120,000 a year, while an experienced specialist can earn up to CHF 150,000 or more. Companies are willing to invest in these experts because of the value they bring.
Data scientist and data analyst: what are the differences between them?
The data analyst focuses on descriptive data analysis, identifying trends, generating reports and providing recommendations based on past information.
Data scientists develop predictive models and advanced algorithms to anticipate future trends and solve complex problems.
In short, the data analyst focuses on the past in order to guide present decisions, while the data scientist explores the future to influence results.
What tools and technologies does a data scientist use?
The success of a data scientist depends largely on his or her ability to master the tools and technologies that are essential for the job. Here is a selection of the resources most commonly used by data scientists:
- Programming languages: Python, R, SQL – essential for data manipulation, statistical analysis and the creation of predictive models.
- Frameworks: TensorFlow, Dataiku, PyTorch, Hadoop, Spark – essential for advanced algorithm development, massive data processing and machine learning.
- Visualisation tools: Tableau, Power BI, matplotlib – to transform complex data into clear, usable visual representations.
- Cloud solutions: AWS, Google Cloud, Snowflake, Microsoft Azure – for large-scale data storage, processing and analysis, with greater flexibility and power.
Which industries recruit the most data scientists in Switzerland?
Switzerland offers many opportunities for data scientists. These experts are particularly sought-after in strategic sectors where data analysis has become an essential lever for innovation and competitiveness.
Sectors actively recruiting:
Banking and finance
Financial institutions use data science to assess risks, detect fraud and optimise investments. Data can also be used to personalise banking services.
Pharmacy and health
This sector relies on data scientists to analyse clinical data, optimise medical research and design treatments tailored to specific patient needs.
Industry and energy
Industrial companies use data to optimise production, plan maintenance and reduce energy costs.
Retail and e-commerce
Data science helps to decode purchasing behaviour, optimise inventories and personalise offers, to improve both customer satisfaction and profitability.
Technology and innovation
Start-ups and technology companies are recruiting data scientists with a view to developing artificial intelligence solutions and automated systems.
What are the challenges and issues facing data scientists today?
The data scientist profession faces complex challenges that combine technology, ethics and organisational strategy. These challenges go beyond the mere manipulation of data: they require in-depth reflection on the responsible use and impact of data, in a world where it plays such a central role.
- Confidentiality and ethics: compliance with data protection standards, such as the GDPR, is of vital importance. Handling sensitive data requires a rigorous approach, to ensure compliance and minimise the risks of breach or leakage.
- Algorithmic biases: machine learning models run the risk of reproducing or amplifying any biases present in the initial data. Detecting, analysing and correcting these biases is essential when it comes to ensuring that the results are accurate and truly representative.
- Data volume and complexity: the rise of Big Data brings with it major challenges, in particular the processing of massive volumes of often unstructured data. This requires the use of innovative tools and methodologies, to transform this data into usable information.
- Rapidly evolving technologies: the frenetic pace of technological advances calls for constant monitoring and continuous learning. Professionals must adapt to master new solutions and maintain a competitive edge.
- Interpreting results: transforming complex analyses into clear, actionable insights for decision-makers remains a crucial challenge. The ability to communicate these results effectively has a direct impact on strategic decision-making.
Freelance or in-house data scientist: what are the differences?
Working as a freelancer or for a company can bring different expectations in terms of quality of life and career prospects. Each path offers opportunities and challenges that influence the data scientist’s career path.
Freelance data scientist
| Advantages | Disadvantages |
|
Greater freedom in choosing assignments, an appreciable amount of flexibility with your time, and pay that is often higher than that of employees on fixed salaries. |
Administrative management and customer prospecting can be time-consuming. Freelancers also have to ensure their technical skills grow without the direct support of a team. |
Data scientist within a company
| Advantages | Disadvantages |
|
Financial stability, access to advanced resources and opportunities for collaboration within multidisciplinary teams. |
Less flexibility around projects and dependence on the company’s strategic areas of focus. |
What career options are available?
The data scientist profession offers a wide range of career prospects, to suit every individual’s ambitions and skills. Whether through technical specialisation, strategic responsibilities or a support role, there are plenty of opportunities for advancement.
- Technical specialisation: data scientists can specialise in fields such as deep learning, natural language processing or computer vision.
- Strategic roles: as they gain experience, they can progress to positions such as Chief Data Officer (CDO), where they determine the overall data strategy.
- Consulting: some choose to become consultants, working with several companies on complex data-related issues.
- Teaching and training: passing on their knowledge by teaching in schools or running training courses is a popular path for data scientists.
- Entrepreneurship: creating a start-up specialising in data or artificial intelligence is an option for ambitious and innovative data scientists.
In conclusion, the data scientist profession constitutes an exciting career opportunity for those with a curious and analytical mind. If you’re ready to take on technical and strategic challenges, why not consider a career in this field?
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