How can you make yourself a more desirable data scientist?
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Data science is one of the most sought-after and lucrative fields in the modern economy, but it is also highly competitive and demanding. If you want to stand out from the crowd and land your dream job, you need to constantly improve your skills, portfolio, and network. In this article, we will share some tips on how you can make yourself a more desirable data scientist and boost your career prospects.
Data science is a fast-evolving domain that requires you to keep up with the latest trends and innovations. You should always be curious and eager to learn new tools and techniques that can help you solve complex problems and deliver value to your clients or employers. For example, you can explore new frameworks and libraries for data analysis, visualization, machine learning, and deep learning, such as pandas, matplotlib, scikit-learn, TensorFlow, and PyTorch. You can also learn new programming languages or paradigms that can enhance your productivity and versatility, such as R, Julia, Scala, or functional programming.
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Gaurav Agrawal
Data Science & Analytics @Walmart | Ex- Analytics Manager at Swiggy
Data Scientists are often considered to be technical people, however the growth comes when we enhance our technical skill set with the business acumen.
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Mohammed Bahageel
Data Scientist / Data Analyst | Machine Learning | Deep Learning | Artificial Intelligence | Data Analytics | Data Modeling | Data Visualization | Python | R | Julia | JavaScript | Front-End Development
A desirable data scientist has strong technical skills in programming, statistics, and machine learning. They possess domain knowledge, analytical and problem-solving abilities, and effective communication and data visualization skills. They are curious learners, have business acumen, and work well in collaborative teams. They also prioritize ethical considerations in data handling and analysis.
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Shivangi Choudhary
Data Science Enthusiast with 11+ years of experience in end to end product delivery with focus on design thinking, dats science strategy and productionalizing ML models at scale
The core skill of a data scientist lies in understanding the business problem , and transforming it to an analytical problem and then orchestrating the solutions around them.. as per the stats , around 87% of data science models are not productionized.. As a data scientist, its equally important to solve for right problems, as much as its important to use the right techniques and algorithms..
One of the best ways to showcase your skills and experience as a data scientist is to build a strong portfolio of projects that demonstrate your ability to apply data science concepts and methods to real-world scenarios. You can create your own projects based on your interests or passions, or you can participate in online competitions or hackathons that challenge you to solve data-related problems. You should also document your projects well and share them on platforms like GitHub, Kaggle, or Medium, where you can attract feedback and attention from other data enthusiasts and potential employers.
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Milan Janosov, Ph.D.
🌏 Founder @Geospatial Data Consulting | 🎯 Network scientist | ✈️ Chief data scientist @Baoba | 📖 Author @Openbooks | 🎖️ Forbes 30u30
I would also add a good, expressive social media (mostly LinkedIn) profile to the portfolio as nowadays; many hirings go through this platform. For my hiring experience when building DS teams, these social 'clues' are a very strong initial filter when screening applications.
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Mohamed Azharudeen
Data Scientist @charlee.ai - Data Science | NLP | Generative AI | AI Research | Python | Deep Learning | Machine Learning | Data Analytics | Articulating Innovations through Technical Writing
To stand out as a data scientist, construct a robust portfolio with diverse projects reflecting real-world application of data science. Contribute to open-source projects or tackle problems in hackathons. Documenting your methodology and results on platforms like GitHub or Kaggle can not only showcase your skills but also invite valuable peer review.
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Jyotishko Biswas
Solves Business Problems using AI | AI Leader | 17+ years exp. in AI | Experienced in Generative AI & LLMs | Guest Speaker on AI - IIM, JK Lakshmipat University etc. | Deployed enterprise-wide AI solutions | ex Deloitte
Building a strong portfolio of projects is a testament of your capabilities which gives confidence to recruiters and your current company's leaders about your capabilities. The data science project portfolio can be captured on Kaggle and / or GitHub which will showcase your work and also get you valuable feedback. Multiple large firms have data science/AI forums with few thousand forum members. Here data scientists within the firm come and showcase their work. Leverage this platform to build your visibility within the firm you are employed.
Data science is not only about crunching numbers and writing code, but also about communicating your findings and recommendations to various stakeholders, such as managers, clients, or users. You need to be able to explain your data insights and solutions in a clear, concise, and compelling way, using appropriate language, visuals, and storytelling techniques. You also need to be able to collaborate with other data scientists and professionals from different domains and backgrounds, such as engineers, designers, or business analysts. You should practice your communication skills by presenting your projects to different audiences, writing blog posts or articles, or joining online communities and forums.
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Taeef Najib
Software Development Engineer (ML) @Sidetrek | 3x Kaggle Expert | Data Science | Machine Learning | Neural Network | Data Analytics | Computer Vision | NLP | MLOps
While some may have an innate ability to communicate effectively, communication is more of a learned behavior. Alongside the technical prowesses, one crucial skill that a desirable data scientist must achieve is great visual, written and verbal communication. Visual communication is useful in telling stories through data, building effective dashboards, etc. Whereas, written communication is important in writing technical documentation, writing blog posts, articles, and reports. Verbal communication is a necessity for great presentations, inter-team communication, and collaboration with stakeholders. One good way to improve your communication is to practice making YouTube videos, writing blog posts, and presenting your work to real people.
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Mohamed Azharudeen
Data Scientist @charlee.ai - Data Science | NLP | Generative AI | AI Research | Python | Deep Learning | Machine Learning | Data Analytics | Articulating Innovations through Technical Writing
Boost your desirability as a data scientist by mastering communication. Translate complex data into actionable insights with clarity and engage with a non-technical audience. Share your findings via blog posts, presentations, and community discussions to refine your storytelling and collaborative skills.
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Milan Janosov, Ph.D.
🌏 Founder @Geospatial Data Consulting | 🎯 Network scientist | ✈️ Chief data scientist @Baoba | 📖 Author @Openbooks | 🎖️ Forbes 30u30
For me a great way of learning to communicate data was reading - a lot. Scientific papers, populizer books, and articles alike.
Another way to make yourself a more desirable data scientist is to network and mentor with other data professionals and experts. Networking can help you expand your knowledge, exposure, and opportunities in the data science field. You can network by attending events, webinars, or meetups, joining online groups or platforms, or reaching out to people you admire or respect. Mentoring can help you share your expertise, experience, and advice with others who are interested in or new to data science. You can mentor by offering guidance, feedback, or support to students, peers, or colleagues, or by volunteering for initiatives that promote data literacy or education.
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Jyotishko Biswas
Solves Business Problems using AI | AI Leader | 17+ years exp. in AI | Experienced in Generative AI & LLMs | Guest Speaker on AI - IIM, JK Lakshmipat University etc. | Deployed enterprise-wide AI solutions | ex Deloitte
Networking and mentoring is another method to establish your credibility and increase visibility. Attend conferences, webinars, network with other data scientists and data science leaders to share your thoughts and views. Stay connected with them after the conference, by sharing your achievements,and doing activities jointly, for example writing an article etc. Mentoring college students who want to build a career in AI will be a win win. You establish your as a data science expert and students will learn from your expertise in the industry.
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Pragati Gupta
Data Analyst | Business Intelligence Analyst | Data Visualization | Data Visualization and Storytelling | Tableau |ETL| SQL | Alteryx | Excel | Python Scripting + Machine Learning Lib| PowerBI | ML |AI
Feedback and suggestions are indispensable for personal and professional growth, especially in the dynamic field of data science. Actively seeking input from peers, mentors, and experts is crucial to refining one's skills and knowledge. Engaging in communities dedicated to data science provides an invaluable platform for exchanging ideas, learning from others' experiences, and staying abreast of the latest trends and techniques.
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Tavishi Jaglan
I write Code| Data Science | Machine Learning | Deep Learning| NLP | Statistics | Mentor| I help people land their first data job within 90 days.
Forge connections within the data science community. Attend conferences, engage in online forums, and build a professional network. Actively participating in the community not only exposes you to diverse perspectives but also opens doors to collaborative opportunities. Additionally, consider taking on a mentoring role; sharing your knowledge not only benefits others but reinforces your own understanding.
Finally, the most important thing you can do to make yourself a more desirable data scientist is to keep learning and improving. Data science is a dynamic and challenging field that requires you to constantly update your skills, knowledge, and mindset. You should always be open to feedback, criticism, and failure, and use them as opportunities to grow and learn from your mistakes. You should also seek new challenges, experiences, and perspectives that can broaden your horizons and spark your creativity. You should never stop being curious, passionate, and ambitious about data science and its potential to make a positive impact on the world.
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Nikhil Kumar
Product Manager (Data Science) | J.P Morgan Chase | Ex-TCS | Ex-Accenture | Data Analytics Certified from Indian Statistical Institute | Statistics | Probability | Machine Learning | NLP | Python | R | SQL | Xceptor
Constant learning is a key to success in every field but when we talk about data science the field itself is testing and bringing new concepts and techniques everyday because of which constant learning is more crucial in data science. Constant learning not only helps data scientists in knowing what are the new things coming in place but it also helps in solving problems, growing mindset etc. It’s not necessary that we need to read 2-4 hours everyday but at least 15-20 minutes every single day on any topic from medium article, HBR article, Mckinsey post or hearing any podcast would really helpfully.
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Tavishi Jaglan
I write Code| Data Science | Machine Learning | Deep Learning| NLP | Statistics | Mentor| I help people land their first data job within 90 days.
Embrace a perpetual learning mindset. The data science landscape is dynamic, with continuous advancements. Regularly invest time in updating your knowledge base, exploring emerging trends, and participating in skill-enhancing activities. This commitment to lifelong learning not only keeps you at the forefront of your field but also positions you as an adaptable and sought-after data scientist.
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Jyotishko Biswas
Solves Business Problems using AI | AI Leader | 17+ years exp. in AI | Experienced in Generative AI & LLMs | Guest Speaker on AI - IIM, JK Lakshmipat University etc. | Deployed enterprise-wide AI solutions | ex Deloitte
AI and data science is experiencing massive innovation. In this environment it's critical to keep learning and improving. Also more people are entering AI as AI is a lucrative career option so the competition is high. Also remain open to feedback, as this field values a professional who has experience. More so because the same work can be done with multiple techniques, since it's a combination of art and science instead of science only. Hence the richness of your experience makes the difference, that's possible if we hear others views and take criticism positively.
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Rashmika Nawaratne
Principal Data Scientist at Endeavour Group
While it's important to build your technical and problem solving mindset, it's equally important to develop a value delivery mindset where you deliver value by incrementally solving the problem. Especially if you are working with business stakeholders. As data scientists from technical background we tend to perfection the solution and deliver once the solution most optimal. To my knowledge, delivery mindset is, designing your solution into phases which you can solve the problem incrementally, and share with the business stakeholders early as possible, so business can start banking value. This way both business can cash benefits early and you get end user feedback early which definitely helps you to develop an optimal solution down the line.
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Olanrewaju Oyinbooke
ML/AI Researcher at COSMOS Lab || Data Analyst || Certified Data Management Professional|| Global Speaker
A few lines from my manager's comment about me. It embodied many soft skills that can make even a Data Scientist more desirable. Olanrewaju stands out as a shining example of what it means to be a collaborative and supportive colleague. He is a dedicated, skilled, and team-oriented engineer who is always ready to lend a hand and contribute to a positive work environment. Olanrewaju stays informed of the latest news and technologies. He has an insatiable curiosity and an amazing ability to absorb and apply new knowledge. Moreover, Olanrewaju generously shares his knowledge and discoveries with colleagues, fostering a culture of continuous learning. Always eager to take on new challenges and adapt to changing circumstances. <word limit>
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Saeid Aliakbar
Data Team Lead at Namafar.ir
I possess a robust foundation in database management, adeptly navigating both relational and non-relational systems. My extensive database knowledge is complemented by a deep understanding of specific industry domains, enabling me to contextualize data-driven insights within the broader business landscape. This fusion of technical expertise and domain knowledge is further enhanced by my strong soft skills, fostering effective collaboration within cross-functional teams. My ability to communicate complex analytical findings to diverse stakeholders showcases not only my technical prowess but also my exceptional communication skills, bridging the gap between data science and business decision-makers with clarity and precision.