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Beyond Technical Know-How: The Critical Role of Soft Skills in Career Growth

Beyond Technical Know-How: The Critical Role of Soft Skills in Career Growth
Reading Time: 3 minutes

As we progress in our careers and take on greater responsibilities within our organizations, it becomes evident that professional skill development is just as crucial as technical competence. While we often associate training with acquiring skills and knowledge directly related to the technical performance of our job, the reality of daily work requires a different set of skills. These include managing diverse workgroups with varying profiles and expectations, developing professional networking, handling difficult clients, communication and interpersonal skills, complex negotiations, and adapting to changing environments. This set of skills is commonly referred to as “soft skills.”

Understanding Professional/Soft Skills

Soft skills encompass competencies related to personal behavior, social relationships, leadership, and emotional management. They enable us to interact and manage our environment effectively. On the other hand, technical skills, known as “hard skills,” are the specific technical abilities required to perform a job. Mastery of technical aspects is essential for any career: a doctor must diagnose accurately, an architect must design creative and functional spaces, and a marketing professional must understand and segment their audience, manage a budget, or develop a marketing plan. However, these competencies, while necessary, are not always sufficient for a successful professional career. We all know individuals who, despite being technically proficient, are stalled in their careers due to their inability to relate optimally with their environment or to leverage their strengths and achievements.

The Shift from Hard to Soft Skills

We are often hired for our technical abilities but grow in our careers through our professional skills. As we accumulate years of professional experience, technical competencies gradually give way to the need to develop other types of soft skills directly related to higher responsibilities, such as team management, leadership, change management, and strategy development. Interestingly, while hard skills are acquired systematically and procedurally through academic life and continuous training, professional competencies are often learned (if at all) through practice, typically in a haphazard manner, late, and usually after making many mistakes due to not having internalized them when assuming responsibilities where their use was critical.

Key Soft Skills

The list of potential non-technical skills to consider in our professional development is extensive, especially given the increasing variability of professional profiles in an ever-more specialized world. However, a preliminary categorization to help distribute this broad list might include:

1. Skills Related to Our Attitude Towards Work:

– Innovation and Entrepreneurship: Constantly seeking to change our environment.

– Professionalism and Integrity: Being a good professional requires being a good person.

– Creativity: Thinking outside the box to find new solutions.

– Growth Mindset: Continuously seeking to learn and improve.

2. Skills Related to Our Future Expectations:

– Clarity of Professional Goals: Knowing what we want professionally.

– Acting in Accordance with Future Goals: Aligning today’s actions with tomorrow’s expectations.

– Wealth vs. Influence: Understanding whether we aim to be rich or to have significant influence can guide our career decisions and reduce frustration from unmet expectations.

3. Skills Related to Managing Our Environment:

– Networking Development: Building a network of contacts with the principle of “giving without expecting, receiving without forgetting.”

– Communication: Effectively conveying ideas and listening to others.

– Negotiation and Difficult Situations Management: Handling complex interactions smoothly.

– Adaptability: Adjusting to a constantly changing environment.

The Evolving Workplace and the Need for Continuous Learning

A generation ago, the functions and competencies associated with job positions remained static for many years. Today, new competencies, challenges, and skills are constantly required, necessitating continuous evolution. Our ability to manage change and adapt is arguably the most critical aspect to consider in our professional training. In a rapidly shifting professional landscape, those who can evolve and adjust their skill set in response to new demands will be the most successful.

In conclusion, while technical skills form the foundation of our professional capabilities, it is the soft skills that enable us to grow, adapt, and excel in our careers. A balanced development of both sets of skills is essential for achieving long-term professional success and personal fulfillment.

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.

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