One of the biggest concerns for students is what they’ll do after graduation. Fortunately, you can’t go wrong with BSc Computer Science. This branch has been evolving rapidly, and the market is hungry for qualified and knowledgeable experts.
The BSc Computer Science degree opens the doors to many job roles. If you’re curious about the concrete positions you can apply for, you’ve come to the right place. We’ll offer a comprehensive BSc Computer Science jobs list to help you find the best match.
BSc Computer Science Jobs for Freshers
When you enroll in your BSc Computer Science program, you can take one of many directions, depending on your preferences.
Entry-Level Software Developer
Are you interested in how to use codes to develop software? If so, this position may be ideal.
Job Description
Software developers are like magicians who take their programming and design knowledge and turn it into fully-functioning software that meets user needs.
Contrary to popular belief, software engineers don’t just create games and apps (although many would like that). These professionals stand behind every single platform, program, machine, and device. Therefore, it shouldn’t come as a shock that the market is desperate for them.
Skills Required
This is an entry-level position. Therefore, real-world experience isn’t at the top of the requirements list for employers. But you do need a BSc Computer Science degree (or be on your way to obtaining it) and knowledge of basic programming languages. There are also some soft skills you’ll need to perform this job. Attention to detail and the ability to work in a team and adapt to a fast-paced environment are common requirements.
Average Salary
How much money you’ll make on this job depends on your employer. On average, you can expect around $80,000 plus bonuses.
Junior Data Analyst
Do you like analyzing large piles of data to extract valuable information and put it to good use? If so, Junior Data Analyst may be your dream job.
Job Description
The basic duties of a Junior Data Analyst involve examining data and applying different techniques to get relevant results. They’re database masters, and it’s their job to know the systems well and figure out the best way to manage them.
While going through data can sound tedious, it’s rewarding in the end. Like finding a needle in a haystack, large piles of information can reveal small but valuable data. These discoveries can pinpoint trends and provide insights that can help a company shape its operations.
Skills Required
If you want to be a Junior Data Analyst, you need to have a knack for distinguishing relevant from irrelevant information. You also need to have an eye for aesthetics, as you’ll need to present your discoveries in a clear and appealing manner. Let’s not forget to mention good time management and great programming and statistics skills, which can be confirmed by your BSc Computer Science degree.
Average Salary
Junior Data Analysts make roughly $60,000 per year.
IT Support Specialist
When we say “computer science,” many imagine passive, somewhat lonesome jobs where you spend your days in silence and write a bunch of code. IT Support Specialists definitely don’t see their jobs this way.
Job Description
If you ask IT Support Specialists why they like their job, many will say, “because it’s rewarding.” And that’s completely true. IT Support Specialists have a somewhat noble role. They troubleshoot technology issues and help others resolve them. “Others” can be other employees in the company or external customers who need help.
Either way, the role of an IT Support Specialist is dynamic and exciting, but it can also be tiring.
Skills Required
In this case, the emphasis is often placed on “soft,” non-technical skills required for the job. These include patience, politeness, and good communication. Of course, you need to understand how different technologies work and be able to troubleshoot problems, often remotely.
A BSc Computer Science (or a similar) degree isn’t always a requirement.
Average Salary
The average salary is approximately $50,000 for entry-level positions.
Web Developer
If you’re looking for a way to combine your love for programming and design and put it to good use, web development is an excellent career path to take.
Job Description
Web developers are creative masters who stand behind websites. They use their programming and design knowledge to come up with websites that are both functional and appealing. Besides creating websites liked by both clients and search engines, web developers maintain them. Therefore, these experts are there throughout a website’s entire “life” and ensure its full functionality at all times.
Skills Required
You don’t need an official degree to be a Web Developer. However, landing a job in this niche isn’t as easy as walking into a company and saying, “I like design and coding.” If you’re serious about becoming a Web Developer, you have to learn HTML and CSS. Then, you need a basic understanding of testing, SEO, and responsive design. Since you’ll often work with other people to “create magic,” you need to be open about teamwork.
Average Salary
On average, Web Developers make around $77,000 per year.
Quality Assurance Analyst
When describing the role of a QA Analyst, some jokingly say it’s perfect for people who love correcting other people’s mistakes. Let’s see what the position entails.
Job Description
QA Analysts test whether a certain product or program is manufactured following the standards set by the industry/company. What does this mean? Let’s say you’re testing a productivity program. To do your job, you’ll first need to create a detailed testing plan describing every stage of the process. Then, you’ll need to execute the testing. You’ll check whether its description matches its performance in terms of compatibility and functions. If there’s any issue, you’ll have to create a report and submit it to the relevant personnel.
Skills Required
Most employers require a BSc Computer Science (or similar) degree when hiring a QA Analyst. Besides that, employers look for other skills that will make them say, “You’re the perfect candidate for the job.” They like someone who pays attention to detail, has a working knowledge of different OSs, and strong analytical skills. Moreover, a great QA Analyst can see how tiny details affect the bigger picture.
Average Salary
QA Analysts make approximately $78,000 per year.
Job Opportunities After Degree Completion
What can you do after BSc Computer Science? With a BSc Computer Science degree in your hands, the world’s your oyster. Here are some directions in which you can drive your career.
Higher Education Options
After completing their studies, many students realize they’re hungry for more. Higher education unlocks new roads and takes ambitious students on an exciting journey. Here are some options to consider:
- MSc Computer Science
- MBA in Information Technology
- Specialized certifications
Networking and Professional Development
Networking and professional development are the winning combo: you get to connect with the people from your branch and acquire knowledge.
- Attend conferences, workshops, and seminars
- Join professional organizations
- Improve your online presence
Internships and Work Experience
Some students want to “skip” internships and go straight to full-time jobs. You shouldn’t consider internships an unnecessary stop along the way but a shortcut to success. When you’re an intern, you’ll work with industry professionals who can offer valuable advice and insight. You can use this time to ask questions and observe what others do. If your superiors like you, your internship can very much turn into a full-time job.
Software Engineer Potential
If you like coding and have excellent analytical thinking skills, software engineering may be the way to go. Software engineers develop anything from video games to complex network systems and software. Other “to-dos” on a software engineer’s list can be software testing, design, and creating presentations.
What do you need to become a Software Engineer? First, you need a degree in computer science or a similar field. Then, you need to be detail-oriented, hardworking, and well-organized. Software engineering isn’t a one-man’s game, so you need to be a team player if you want the best results.
Depending on the company’s policy, being a Software Engineer can set you up for other roles like Tech Lead, Technical Architect, and Chief Technical Officer (CTO).
Tips for Freshers to Stand Out on the Market
It’s undeniable that the computer science industry is flourishing. But that doesn’t mean jobs grow on trees. Since more and more people are interested in the industry, standing out is becoming more challenging. Here’s how to set yourself apart:
- Update your resume and portfolio.
- Make connections with the people from your branch (through online platforms and/or in person).
- Keep up with the most recent industry trends.
- Focus on your soft skills, as they can be the X factor for landing an internship or a job.
Land the Best Jobs in the Industry
If you were wondering, “Can I get a job after BSc Computer Science?” the answer is absolutely! Computer scientists are in high demand, and with a BSc Computer Science degree in your hands, you can drive your career in the desired direction.
Besides your degree, don’t forget about the not-so-secret ingredient in your recipe for success: constant improvement and development.
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From the same author
Read the articleSource:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Read the articleSource:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
-
Go to all authors
Related posts
Source:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Source:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
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