Freelance Data Scientist: Job description, missions, salary

The Data Scientist, a true data architect, plays a key role in modern businesses. Her freelance mission is to explore and harness the power of data to solve problems, make informed decisions, and drive innovation. Let's dive into the details of this complex and exciting profession.

Job description

Decision support

Data scientists are the pathfinders of the 21st century. They inform businesses by using data to guide decision making.

Analysis of trends and patterns: Their purpose is to examine data to identify trends, patterns, and significant correlationships. For example, this may include detecting customer consumption trends or identifying factors that influence production.

Predictions and predictions: By using machine learning and statistical modeling techniques, they are able to create accurate forecasts and predictions. This can be applied to forecasting sales, demand, costs, or any other parameter relevant to the business.

Risk assessment: With accurate data analysis, it is also possible to assess risks. For example, in the financial sector, these professionals contribute to the assessment of credit risks by analyzing the financial data of borrowers.

Customization: They contribute to the personalization of products and services. They use the history of customer interactions to recommend products that are tailored to their preferences.

Performance measurement: Data scientists develop key performance indicators (KPIs) to assess the effectiveness of business strategies. They design performance dashboards to allow managers to keep track of data changes.

Resource Management: Depending on the business area, these experts can help businesses manage their resources effectively. This may include inventory management, human resource allocation, or production capacity planning.

Reduction of human errors: Automating decision-making processes based on data can reduce human error.

Data control

Data control is one of the central elements of the job. These professionals collect digital information from a variety of sources, such as databases, CSV files, real-time feeds, or IoT sensors. This profusion of raw data must be cleaned, organized, and structured to become an exploitable resource.

Data collection is an essential step, and it may involve using APIs to extract them from external sources. For example, a social media company may extract data on user behavior, including likes, shares, and comments. This collection must be methodical, planned and adapted to the needs of the project.

Once the information is collected, it should be Cleaned. Raw data is rarely perfect, and it can contain errors, missing values, duplicates, and other inconsistencies. They use cleaning techniques to eliminate these problems and ensure that the information is of high quality and reliable.

La Data transformation It is a crucial step. Once Cleaned, They May Require Transformations to Become Usable. This may include format conversion, aggregation, creation of new variables, or other operations to obtain data that is structured and ready for analysis.

They work with management systems to store, organize, and query results. They Need to Master Relational databases (SQL) and databases of NoSQL data, according to the needs of the project. Data security is a major concern. They are responsible for ensuring that they are protected against unauthorized access and leaks in accordance with data protection regulations.

Managing massive amounts of intelligence has become routine with the rapid growth of information. Freelance or permanent data scientists should be familiar with massive management technologies and methods, including using environments like Hadoop and Spark for distributed data processing.

Accessible Returns

The ability to make the results of data analyses accessible to a broad audience is essential. Specialists often translate complex information into graphical visualizations, infographics, or reports that are easy to understand. A concrete example would be creating interactive dashboards to help managers monitor business performance in real time, using Looker (formerly DataStudio) for example.

Communicating results is a key part of this process. They should be able to explain their findings clearly and concisely, highlighting business implications.

What are the main missions of a Data Scientist

Extraction and structuring of data

Extracting data can be compared to collecting raw materials. These professionals extract raw data from a variety of sources. Once collected, they need to be cleaned up and structured to eliminate errors and inconsistencies. This could mean eliminating missing values, resolving format inconsistencies, or removing duplicates.

Development of artificial intelligence algorithms

Data scientists develop machine learning and deep learning models to solve specific problems. For example, a data scientist working for a video streaming platform can develop a recommendation algorithm that analyzes users' viewing behavior to suggest new shows or movies to watch. This task requires a thorough knowledge of machine learning techniques, the selection of appropriate algorithms, and the implementation of these models.

Active participation in projects

It is important to remember that this is not a job that is done in isolation. They are essential members of the project team, collaborating with software engineers, business analysts, experts in specific fields, and other professionals. They need to integrate data and analytics into business processes. For example, in developing software products, they may be involved in creating features based on data analytics, such as product recommendations or improved search filters.

Technology Watch on Data Science Tools

Technology is constantly evolving. They must remain at the forefront of these advances. They monitor the latest data science tools, libraries, and frameworks, or visualization tools like Power BI. They also need to be up to date with the latest research in artificial intelligence and machine learning to ensure that their analysis methods are up to date.

Skills required

Becoming a data scientist requires a diverse set of skills, ranging from technical mastery to essential interpersonal qualities.

Mastery of tools

They need to be comfortable with a multitude of tools and technologies. In the field of machine learning, they use frameworks like TensorFlow and PyTorch to build models. To manage vast amounts of information, they master environments like Hadoop and Spark. They work with SQL and NoSQL databases to store and retrieve information. They have development knowledge with programming languages such as Python, R, Java, or C++. This technical versatility is crucial for success.

Project Management And teams

Data Scientists aren't just lonely data analysts. Although this post can be done as a freelancer, they work as a team on interdisciplinary projects and should be able to manage their time effectively. Collaborating with other team members, including engineers, designers, and experts in specific fields, is a valuable skill. They need to be able to translate business needs into technical solutions.

Force of Proposal

Creativity is a major asset. They need to be able to generate innovative ideas to solve problems and improve processes.

What training to become a freelance data scientist?

Becoming a data expert generally requires a solid academic background, although the paths vary.

Most have at least a Master's degree (bac+5), often in computer science, statistics, mathematics or related sciences.

However, many people are succeeding in this field through online training, data science bootcamps, and self-learning.

Certifications like those from Google Cloud or Amazon Web Services can also strengthen your resume.

What are the career prospects

The career prospects are promising.

With the continued growth of data analytics and artificial intelligence, there are plenty of opportunities for those who are proficient in this field. After gaining experience, you can progress to:

• Management positions
• Posts from artificial intelligence experts
• A Data Engineer position

What is the salary of a Data Scientist

The remuneration of these professionals varies according to experience, geographic location, and industry. In general, employees are competitive.

To give a concrete example, in Paris, a junior data scientist can expect an annual salary of between 40,000 and 60,000 euros.

Whereas a senior can earn over $80,000 per year. In some areas where demand is high, these numbers may be even higher.

This job can largely be done as a freelancer. Unlike permanent contracts, they are not paid with a salary, but with a TJM (average daily rate). Discover the TJM of a data scientist.

Employment and sector of activity

They are in demand in a wide variety of sectors, including finance, healthcare, healthcare, healthcare, technology, e-commerce, logistics, media, and many more.

Their data analytics expertise is valuable across many industries, which means job opportunities abound.

Example of a data scientist job description:

Definition: Precise analysis of data to respond to company problems (in marketing as well as in management). Support strategic decision-making and optimize the customer experience. It is the expert who makes the numbers speak

Main objective: Transform raw data into usable and intelligible information in order to find ways to make intelligent and optimal use of data.

Mission Data Scientist:

• Generating knowledge through numbers:
  • Detecting and understanding complex behavioral patterns or trends
  • Elaboration of working hypotheses and projects
  • Exploring and studying data using Machine Learning Algorithms
• Designing new data driven digital products:
  • Design of digital products based on analyzed information and generation of results algorithmically
  • Construction of a computer algorithm to respond directly to the problem (creation of tools that can be operated on a large scale).

Necessary studies: Bac + 4 or Bac + 5 in computer science, management, statistics or marketing (+ doctorate in computer science, mathematics, statistics or data modeling)

Hard Skills:

• Knowledge in mathematics, programming, machine learning concepts
• Mastering SQL queries
• Management of unstructured data (social media, video or audio feeds,...)
• Data Visualization

Soft Skills:

• Curiosity and Analytical Skills
• Good Communication
• Ability to Detect Problems to Be Solved