Contact us

AI revolution, redefining and reinventing tech talent (part 3)

Reading Time: 3 minutes
We conclude the series of articles on how the AI revolution has modified and continues to modify the IT Talent environment, today moving from the realm of professional talent to the realm of companies.
Just as important as a certified professional who conveys the necessary confidence in their skills, is to be able to be sure that the organization or company developing products and services associated with AI can demonstrate a reasonable level of auditing and certification.
In this environment, the investment and return are not yet so clear, especially because many of the certifications or audits that are launched do not yet have the certainty of their continuity in the medium term. In the changing environment and exponential development of AI it is quite logical that this is the case.
A good basis to start with is to be already certified or “have the seal”, as they say, of standards already well tested and widespread in information security, process management and quality (ISO 27001, ISO 9001, National Security Scheme, etc.). If our organization has already passed through here, facing the more IA and Data oriented certifications will be much simpler, and above all much more accessible.
Let’s not forget that some of the aspects that most concern large companies when adopting AI, in addition to the ethical implications, are those aspects related to compliance, data security and location, privacy, etc.

Standards and Certification Organizations

ISO (International Organization for Standardization)

  • Description: This ISO committee develops international standards for artificial intelligence, including aspects of security, reliability and ethics.

IEEE (Institute of Electrical and Electronics Engineers)

  • Description: IEEE offers a number of courses and certifications focused on artificial intelligence and AI ethics.
We focus on the ISO because of its international diffusion mainly.
ISO/IEC JTC 1/SC 42 is a joint subcommittee of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) dedicated to standardization in the field of artificial intelligence. More details on its functions, focus areas and the standards it develops are presented below:

ISO/IEC JTC 1/SC 42: General Overview

Functions and Objectives

  • Establishment of Standards: Develop international standards that address artificial intelligence technologies and applications.
  • Coordination: Coordinate with other ISO and IEC technical committees and subcommittees, as well as other organizations, to ensure consistency and avoid duplication.
  • Evaluation and Audit: Evaluate the social, legal and ethical implications of artificial intelligence technologies.
  • Adoption Facilitation: Facilitate the adoption of AI standards by industry, governments and other bodies.

Areas of Focus

  • Big Data: Standards related to the management and analysis of large volumes of data.
  • Machine Learning: Standards for the development, training, evaluation and application of machine learning models.
  • Governance and Ethics: Guidelines and standards on the responsible and ethical use of AI.
  • Trusted AI: Standards that ensure transparency, explainability, security and privacy in AI systems.

Main Standards Developed

  • ISO/IEC 22989:2022 – AI Concepts and Terminology: Establishes common terminology and fundamental concepts in the field of artificial intelligence, providing a uniform basis for the development of other AI standards.
  • ISO/IEC 23053:2022 – AI Framework: Provides a general framework for the development and implementation of artificial intelligence systems, covering aspects such as architecture, life cycle and best practices.
  • ISO/IEC 24027:2020 – Data Quality Assessment for Machine Learning: Standards for assessing the quality of data used in the training and validation of machine learning models.
  • ISO/IEC 20546:2019 – Big Data Overview and Vocabulary: Provides an overview and standard vocabulary for key terms and concepts related to Big Data.
  • ISO/IEC TR 24028:2020 – Assessment of Machine Learning Classification Performance: Guidelines for the assessment of the performance of machine learning classification models, including metrics and evaluation methods.
  • ISO/IEC TR 24030:2021 – Implementation of AI: Provides guidelines for the implementation of AI systems, covering technical, organizational and ethical aspects.
  • ISO/IEC TR 24028:2021 – Guidelines on AI Ethical and Societal Considerations: Guidelines on ethical and societal considerations in AI development and implementation, including issues such as transparency, accountability and inclusiveness.

Relevance and Application

  • Industry: The standards developed by ISO/IEC JTC 1/SC 42 are crucial for industry, as they provide a structured and recognized framework for developing and evaluating AI technologies.
  • Governments: Governments can use these standards to formulate policies and regulations to ensure the ethical and responsible development of AI.
  • Academia and Research: Academic and research institutions can adopt these standards to guide their projects and ensure interoperability and ethics in their work.
  • Society: By addressing ethical and governance issues, these standards help mitigate risks and ensure that AI technologies benefit society as a whole.

Participation and Continuous Development

The ISO/IEC JTC 1/SC 42 subcommittee works continuously to develop new standards and update existing ones, based on evolving technology and market needs. Members include experts from various countries and organizations, ensuring a global and multidisciplinary representation in the standardization process.
In this context, with the speed of development of AI and Data Science and their interrelationship, standards already in use continue to be refined and new ones developed to meet the challenges that AI is generating. 
When it comes to overcoming possible barriers to AI adoption, the certification and auditing framework is a fundamental element, and it is also the one that is developing more slowly, but it is also advancing continuously to provide a secure framework for AI application and to avoid problems that may undoubtedly arise in the future.
Antonio Castaño

Autor Antonio Castaño Romero

AI revolution, redefining and reinventing tech talent (part 2)

IA Revolution
Reading Time: 5 minutes
Today we continue with the analysis of how the AI revolution is redefining the Talent framework in the IT environment, if in the first article we focused on the new professional profiles, in this second part we will emphasize how internationally recognized certifications are being created that support the knowledge of professionals and what regulated training is accessible to future AI and Data professionals.  In the third, and last chapter of this series, we will review the certifications oriented to organizations and companies, which validate their expertise, security and ethics when applying AI and which are also having an exponential growth within the AI revolution.

Professional certifications

In everything related to AI and its applications, the European environment is a step behind the USA, in the aspect of professional certification as well, the most globally recognized professional certifications are born from many of the big players that are driving from minute 1 this revolution and that, unfortunately, are not European.  We expose certifications with an important path and a globally recognized prestige, not all of them are here, but this would be our first selection. Some of them complement in an ideal way the formal training that we will analyze later, on the other hand, as is well known: there are already many options for training in all fields of AI and Data that can be a good previous and more self-taught step before investing time and money in some of the options presented here, in the main cloud training platforms the impact of AI and Data is, today, simply brutal:

Certifications in Artificial Intelligence and Data Science

Certified Artificial Intelligence Practitioner (CAIP)

  1. Organization: CertNexus
  2. Designed for professionals who wish to demonstrate their skills in the design, development and management of artificial intelligence solutions.

Google Professional Machine Learning Engineer

  1. Organization: Google Cloud
  2. Validates a professional’s ability to design, build and manage machine learning models on Google Cloud Platform.

Microsoft Certified: Azure AI Engineer Associate

  1. Organization: Microsoft
  2. It is aimed at AI engineers who use Azure Cognitive Services, Azure Machine Learning and Knowledge Mining to design and implement AI solutions in Microsoft Azure.

IBM AI Enterprise Workflow Certification

  1. Organization: IBM
  2. Offered in partnership with Coursera, this certification covers the full cycle of artificial intelligence application development, from data preparation to implementation.

TensorFlow Developer Certificate

  1. Organization: TensorFlow (Google)
  2. Aimed at developers who wish to demonstrate their competence in the use of TensorFlow for the development of machine learning and deep learning models.

Data Science Certifications

Certified Analytics Professional (CAP)

  • Organization: INFORMS
  • Validates the knowledge and ability to apply advanced analytical principles and solve complex business problems.

SAS Certified Data Scientist

  • Organization: SAS
  • Designed for professionals who want to demonstrate their skills in data manipulation, advanced analytics and implementation of predictive models.

Cloudera Certified Professional Data Engineer (CCP Data Engineer)

  • Organization: Cloudera
  • Validates skills to develop data processing solutions and create data workflows using Cloudera technologies.

AI Ethics Certifications

AI Ethics and Governance Certification

  • Organization: The Alan Turing Institute
  • It covers ethical and governance issues in the development and implementation of AI systems.

Regulated training

We analyze here only the available in the European environment, the available in the USA would give us for another article. The power of this training at international level is very important due to the fact that the educational institutions that promote it have a very high prestige. It should be noted that, unlike the professional certifications we analyzed at the beginning of this article, they have a more transversal approach, ranging from technical aspects to AI application ethics. It is foreseeable that this offer will increase exponentially in the coming years, but today we already have high training possibilities that guarantee knowledge of AI and Data Science. From a professional point of view, the employability of a professional with this training is practically immediate. We highlight here some of the most outstanding options:

Regulated Training in Spain

University degrees

Degree in Artificial Intelligencel
  • Polytechnic University of Madrid (UPM): Offers a degree in Artificial Intelligence with a multidisciplinary approach, ranging from programming to AI ethics.
Degree in Data Science and Artificial Intelligence
  • Carlos III University of Madrid (UC3M): Combines data science with artificial intelligence, providing a solid foundation in mathematics, statistics and programming.

Master’s degrees

Master’s Degree in Artificial Intelligence
  • Polytechnic University of Catalonia (UPC): This program focuses on advanced AI techniques, machine learning and natural language processing.
Master’s Degree in Data Science and Computer Engineering
  • University of Granada (UGR): It offers training in data science, big data and artificial intelligence.
Master in Artificial Intelligence
  • National University of Distance Education (UNED): Distance learning program that covers from theoretical fundamentals to practical applications of AI.

PhD´s

PhD in Artificial Intelligence
  • Polytechnic University of Madrid (UPM): Focused on advanced research in AI, covering areas such as deep learning, computer vision and robotics.

Regulated Training in the European Union and UK

University degrees

BSc in Artificial Intelligence
  • University of Amsterdam (The Netherlands): English-language program that provides a solid foundation in algorithms, machine learning and AI ethics.
BSc in Data Science and Artificial Intelligence
  • Maastricht University (The Netherlands): It combines data science and AI, with a focus on practical applications and interdisciplinary projects.

Master’s degrees

Master in Artificial Intelligence
  • KU Leuven (Belgium): This master’s degree covers a wide range of topics in AI, including machine learning, robotics and natural language processing.
MSc in Artificial Intelligence
  • University of Edinburgh (United Kingdom): One of the most recognized programs in AI, with a focus on research and practical applications.
EIT Digital Master School: MSc in Data Science
  • European Institute of Innovation and Technology (various European universities): It offers a combination of data science and AI, with cross-university mobility and a focus on innovation and entrepreneurship.

PhD´s

PhD in Artificial Intelligence
  • University of Cambridge (United Kingdom): Focused on cutting-edge AI research, with projects in areas such as computer vision, natural language processing and AI ethics.
PhD in Machine Learning
  • ETH Zurich (Switzerland): This program focuses on advanced research in machine learning, with applications in various scientific and technological areas.

Specialized Courses and Certifications

European Association for AI (EurAI)
  • It offers certifications and specialized courses in AI, including continuing education programs for professionals.
Coursera & edX
  • Platforms that collaborate with European universities to offer courses in AI, data science and machine learning, many of which are accredited.
AI4EU Academy
  • European Union initiative to provide AI training through online courses and educational resources.

European Union Initiatives and Programs

AI4EU (Artificial Intelligence for Europe)
  • European platform for AI collaboration, including training, research and policy development. AI4EU Academy offers educational resources and training programs in AI.
Horizon Europe
  • EU research and innovation framework program that funds AI projects and training, promoting collaboration between academic, industrial and governmental institutions.
As we have been able to analyze throughout the last two articles, the impact of AI, more than demonstrated in all the aspects we know within the field of Information Technologies, is spreading increasingly in all the fields that affect the talent of professionals. From the creation of new profiles and the redefinition of many of the existing ones to the formal training they need for their success in the professional market, we have covered other important aspects that make it very clear that this revolution is no longer just a future, it is present and has come to stay.
Antonio Castaño

Autor Antonio Castaño Romero

Learn at least 5 of the key IT profiles for Innovation and Efficiency in the Pharmaceutical Sector

Reading Time: 2 minutes

The pharmaceutical sector faces constant challenges that require innovative and efficient solutions. In this context, Information Technology (IT) profiles have become essential to driving digital transformation, improving operational efficiency, and accelerating innovation. All of these profiles are among the most in-demand in the market; being able to invest in them while minimizing risks and adaptation periods is one of the keys to success, along with flexibility and adaptation to the needs of each moment.

Below, we will explore some of the most sought-after IT profiles and their impact on the pharmaceutical industry.

1. Data Scientists:

Data scientists are crucial in analyzing and interpreting large volumes of data. In the pharmaceutical sector, their ability to handle and analyze clinical, genomic, and market data allows companies to identify patterns, optimize research and development (R&D) processes, and predict patient behavior trends. Thanks to their work, companies can make more informed decisions and develop more effective and personalized medications.

2. Software Developers:

Software developers play a vital role in creating applications and platforms that facilitate data management, process automation, and internal and external communication. In the pharmaceutical industry, these professionals develop web content applications, supply chain tracking platforms, and mobile applications that improve patient adherence to treatments. Their work not only enhances operational efficiency but also contributes to the safety and quality of products.

3. Cybersecurity Experts:

Cybersecurity is a priority in the pharmaceutical sector, given the handling of sensitive and confidential data. Cybersecurity experts protect systems and information against threats and cyberattacks. They implement defense strategies, conduct security audits, and ensure compliance with regulations and standards. Their work ensures that critical information is protected, which is vital for maintaining the trust of patients and business partners.

4. Cloud Engineers:

The adoption of cloud solutions has transformed how pharmaceutical companies manage their data and applications. Cloud engineers are responsible for designing, implementing, and maintaining secure, scalable, and efficient cloud infrastructures. This enables companies to store large volumes of data, facilitate global collaboration, and reduce operational costs. Additionally, the flexibility of the cloud accelerates the development of new medications and treatments.

5. Artificial Intelligence (AI) Specialists:

Artificial intelligence is revolutionizing the pharmaceutical sector. AI specialists implement algorithms and models that can analyze large data sets, identify new drug candidates, optimize supply chain logistics, and personalize patient treatments. AI not only speeds up the drug discovery process but also improves the precision and efficacy of treatments, leading to better patient outcomes.

The Importance of Experience in the Pharmaceutical Sector:

Specific experience in the pharmaceutical sector is crucial for IT professionals, as the industry is highly regulated, and medical-legal validation processes are complex and rigorous. Having a deep understanding of these regulations and processes allows IT experts to ensure that technological solutions meet the required quality and safety standards. This experience provides significant advantages, such as the ability to anticipate and mitigate regulatory risks, ensure data integrity and traceability, and facilitate audits and approvals by health authorities. Ultimately, having IT professionals with pharmaceutical sector experience enhances the efficiency and reliability of processes, translating into faster and safer market introduction of new treatments.

IT profiles are playing an increasingly crucial role in the pharmaceutical sector. Their ability to innovate, protect, and optimize processes is essential to meeting the industry’s current and future challenges. The integration of these technical skills with pharmaceutical knowledge is leading to significant advances that benefit both companies and patients. In a world where technology and health are increasingly intertwined, having the right IT talent is essential for the sector’s success and sustainability.

#Quodem #TaaS #Pharma #Innovation #Talent #FutureOfWork

Antonio Castaño

Autor Antonio Castaño Romero

AI revolution, redefining and reinventing tech talent (part 1)

Revolución IA / IA Revolution
Reading Time: 4 minutes
According to the most serious market studies, a new professional framework for the development of AI is already being defined, although this process is far from complete and we need to be very vigilant, as the point of stabilization is only just on the horizon. This fact, in a sector such as pharmaceuticals, which is one of the fastest developing sectors for AI, has created a pressing need for a better understanding of the context surrounding the impact on the talent required for its implementation.
The rise of artificial intelligence (AI) has led to the creation and redefinition of several specialized professional profiles within the IT field, each with specific roles in the market.
Some are newly created, the most specific to AI, and others are redefined and deepened within existing profiles, almost always linked to an element that is very close to AI: the processing and analysis of data in all its aspects.
The symbiosis between AI and Data Science is such that it is sometimes difficult to draw a clear boundary; analyzing the skills of each profile involved is a good way to start.

Data Scientist

Data Scientists are experts in analyzing and processing large volumes of data to extract valuable information to guide strategic decision making.They use machine learning techniques and statistical analysis to build predictive models and present understandable results to business stakeholders.
Functions:
  • Analysis and processing of large volumes of data.
  • Creation of predictive and machine learning models.
  • Data interpretation for strategic decision making.
  • Data visualization and presentation of results to stakeholders.

Data Engineer

Data Engineers are responsible for designing and building systems that facilitate the efficient processing and storage of large amounts of data. Their work is crucial to ensure that data is available and of high quality for analysis and modeling.
Functions:
  • Designing and building data processing systems.
  •  Integration and management of databases and data lakes.
  • Creation of data pipelines for analysis and modeling.
  • Data quality assurance and data availability.

Data Analyst

Data Analysts are in charge of extracting and analyzing data to obtain insights to support decision making. They create reports and dashboards that help identify trends and patterns, providing key information for business strategies.
Functions:
  • Extracting and analyzing data for actionable insights.
  • Creation of reports and dashboards.
  • Supporting data-driven decision making.
  • Identification of trends and patterns in data.

Machine Learning Engineer

Machine Learning Engineers specialize in the development and implementation of algorithms and models that allow machines to learn from data. Their focus is on optimizing and deploying these models in production environments to solve complex problems.
Functions:
  • Development and implementation of machine learning algorithms and models.
  • Optimisation of models for performance and scalability.
  •  Implementation of machine learning solutions in production.
  •  Maintenance and continuous improvement of AI models.

AI Engineer

AI Engineers are dedicated to developing artificial intelligence systems that emulate human behavior. They use advanced technologies such as natural language processing and computer vision to create innovative solutions that are integrated into products and services.
Functions:
  • Development of AI systems that mimic human behavior.
  •  Implementation of technologies such as natural language processing (NLP) and computer vision.
  • Integration of AI into products and services.
  • Collaboration with other technical and business teams for the implementation of AI solutions.

AI Researcher

AI Researchers focus on exploring new techniques and algorithms in the field of artificial intelligence. Their work includes publishing scientific papers and collaborating with academic institutions to advance the understanding and application of AI.
Functions:
  • Conducting advanced research in new AI techniques and algorithms.
  •  Publication of scientific papers and presentation of findings at conferences.
  • Collaboration with academic and research institutions.
  •  Exploration of new applications and emerging technologies in AI.

AI Ethics Specialist

AI Ethics Specialists assess the ethical implications of artificial intelligence systems. They develop policies and guidelines to ensure the responsible use of AI, promoting transparency and fairness in its application.
Functions:
  • Assessment of ethical implications of AI systems.
  • Developing policies and guidelines for the responsible use of AI.
  • Monitoring compliance with ethical standards in AI projects.
  • Promoting transparency and fairness in the use of AI technologies.

Chatbots and Virtual Assistants Developer

They create programs that interact with users using natural language.They implement natural language processing techniques to improve language understanding and generation, integrating these systems with various platforms and services.
Functions:
  • Design and programming of chatbots and virtual assistants.
  • Implementation of NLP techniques for language understanding and generation.
  • Integration of chatbots with existing platforms and services.
  • Continuous improvement of interaction and user experience.
 

AI Robotics Engineer

AI Robotics Engineers develop intelligent robots with autonomous capabilities.They integrate computer vision and machine learning systems into robots, programming autonomous behaviors and validating their performance in real environments.
Functions:
  •  Development of intelligent robots with autonomous capabilities.
  •  Integration of computer vision and machine learning systems in robots.
  • Programming of autonomous behaviors and decisions in robots.
  • Testing and validation of robots in real environments.
The change we are experiencing in the world of data thanks to AI, and the need to rethink or reinvent many of the technical profiles needed in the Pharma sector to implement technological innovation. is forcing us to adapt to our organizations.
In this context of change and growth, the needs for new professionals who know how to adapt and surf in change convert companies dedicated to attracting talent in the ideal partner to get flexibility and adaptability necessary, minimizing the risk of creating internal structures in our organization that may become obsolete before their time.
In a second article we will take a closer look at how, in the context of formal training, AI is redefining the training of specialized talent and in which certification or auditing framework we can start moving to make sure we are moving in the right direction.

Bibliography

Antonio Castaño

Autor Antonio Castaño Romero