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# introduction
We understand that the pace of data science is growing makes it difficult for data scientists to keep up with all new technologies, demands and trends. If you think knowing Python and machine learning will help you get your job done in 2025, I'm sorry for breaking it, but that's not the case.
To get a good opportunity in this competitive market, you have to go beyond basic skills.
I'm not only mentioning technical skills, but also soft skills and understanding of business. You may have come across such an article before, but trust me this is not a clickbait article. I actually did my research to highlight areas that are often overlooked. Please note that these recommendations are based purely on industry trends, research papers, and insights I have gathered from talking to a few experts. So let's get started.
# Technical skills
// 1. Graph analysis
Graph analysis is very underrated, but very useful. By turning data relationships into nodes and edges, you can help you understand the relationships between data. You can apply graphs on fraud detection, recommended systems, social networks, or on connected locations. While most traditional machine learning models struggle with relational data, graph techniques make it easier to catch patterns and outliers. Companies like PayPal use it to identify fraudulent transactions by analyzing relationships between accounts. Tools like Neo4J, NetworkX, and Apache Age can help you visualize and work with this type of data. If you're serious about getting deeper into areas like finance, cybersecurity, e-commerce, and more, this is one of the skills that will set you apart.
// 2. Edge AI implementation
Edge AI is essentially about running machine learning models directly on devices without relying on cloud servers. Everything from the clock to the tractor is now very important as it's smarter. Why is this important? This means faster processing, more privacy, and less reliance on internet speeds. For example, manufacturing can predict failures before sensors on the machine occur. John Deere uses it to detect crop diseases in real time. In healthcare, wearables process data instantly without the need for cloud servers. If you're interested in Edge AI, look into protocols like Tensorflow Lite, ONNX Runtime, MQTT and CoAP. Also consider Raspberry Pi and low power optimization. According to Fortune Business Insights, the Edge AI market will grow from USD 270.1 billion in 2024 to USD 2698.2 billion by 2032, so it's not a hype.
// 3. Interpretability of the algorithm
Let's be authentic, building a powerful model is cool, but what if you can't explain how it works? It's not that cool anymore. Especially in the high-stakes industry, such as healthcare and finance, where explanability is essential. Tools like Shap and Lime can help you break down decisions from complex models. In healthcare, for example, interpretability can highlight why the AI system flagged patients as high risk. This is important for both ethical AI use and regulatory compliance. It may also be better to build something inherently interpretable, such as a decision tree or a rule-based system. As Cynthia Rudin, an AI researcher at Duke University, says: “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.” In short, interpretability is not an option when a model affects real people, but it is essential.
// 4. Data privacy, ethics, and security
This is not just for the legal team. Data scientists need to understand that too. One of the wrong moves using sensitive data can lead to lawsuits and fines. Privacy laws such as the CCPA and GDPR are expected to know techniques such as privacy differences, isomorphic encryption, and federation learning. Ethical AI is also attracting serious attention. In fact, 78% of consumers surveyed believe that companies must commit to ethical AI standards, while 75% say their trust in their data practices directly affects their purchasing decisions. Tools such as IBM's Fairness 360 can help you test bias in your dataset and model. TL;DR: If you are building something that uses your personal data, please explain how you are doing it better, knowing how to protect it.
// 5. Automl
Automl tools are becoming a robust asset for any data scientist. Automate tasks like model selection, training, adjusting hyperparameters, and more, so you can focus more on real problems rather than getting lost in repetitive tasks. Tools like H2O.AI, Datarobot, Google Automl, and more help to make things faster. But don't twist it, Automl isn't about replacing you, it's about boosting your workflow. Automl is not a pilot, it is a co-pilot. You still need the brain and context, but this can handle the growl.
# Soft Skills
// 1. Environmental awareness
This may surprise some, but AI has carbon footprint. Large-scale models of training take on crazy amounts of energy and water. As a data scientist, you are responsible for making technology more sustainable. Optimizing code, selecting efficient models, working on green AI projects, etc., this is the space where technology achieves its goals. Microsoft's Planetary Computer is a great example of using AI as an environmental good. As stated by the MIT Technology Review, “AI's carbon footprint is a wake-up call for data scientists.” Being a responsible data scientist in 2025 also includes thinking about the environmental impact.
// 2. Dispute resolution
Data projects often include a combination of engineers, product people, business heads, and people who trust me. Not everyone agrees at all times. That's where the conflict resolution begins. It's a big deal to be able to handle differences without stalling progress. It ensures that teams remain focused and move forward as a unified group. A team that can efficiently resolve conflicts is simply productive. Here, being agile thinking, empathy and solution orientation is enormous.
// 3. Presentation skills
You can build the most accurate models in the world, but if you can't explain them clearly, you won't go anywhere. Presentation skills are what separate great data scientists from others, especially by explaining complex ideas in simple terms. Whether you're a CEO or talking to a product manager, the way in which you communicate insights is important. In 2025, this is not just “great” but also a central part of the job.
# Industry-specific skills
// 1. Domain knowledge
Understanding your industry is important. You don't have to be a financial expert or a doctor, but you need to get the basics of how things work. This will help you to ask better questions and build a model that actually solves problems. In healthcare, for example, knowing about medical terminology and regulations like HIPAA makes a huge difference in building reliable models. In retail, customer behavior and inventory cycles are important. Essentially, domain knowledge links technical skills to actual impact.
// 2. Regulatory compliance knowledge
Let's face it, data science is no longer free. With GDPR, HIPAA and the current EU AI law, compliance is becoming a core skill. If you want to keep your project live, you need to understand how to build with these regulations in mind. Many AI projects are delayed or blocked simply because no one is thinking about compliance from the start. With 80% of AI projects facing compliance delays, knowing how to make your system auditable and regulated-friendly gives you a serious advantage.
# I'll summarize
This was my breakdown based on research I've been doing recently. If you have more skills in mind or have insights to add, I love to hear them. Please drop it in the comments below. Let's learn from each other.
Kanwal Mehreen He is a machine learning engineer and a technical writer with a deep passion for the intersection of data science and medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT.” As APAC's Google Generation Scholar 2022, she advocates for diversity and academic excellence. She is also recognized as Teradata diversity for technology scholars, MITACS Globallink Research Scholar and Harvard Wecode Scholar. Kanwar is an avid advocate for change and has founded a fem code to empower women in the STEM field.
