At a Glance
- Tasks: Develop and train cutting-edge computer vision models for real-world applications.
- Company: Join a growing tech business focused on advanced AI systems.
- Benefits: Competitive salary, bonus, and the chance to work remotely.
- Other info: Collaborative team environment with exciting challenges and growth opportunities.
- Why this job: Make a real impact by deploying models in live environments.
- Qualifications: Strong experience in computer vision, deep learning, and Python programming.
We’re supporting a growing technology business developing advanced AI systems that operate in complex, real-world environments. Their work focuses on applying computer vision and machine learning to image and video data, solving challenging problems around detection, classification, and real-time analysis. This is a great opportunity to work on systems where models are deployed into live environments, rather than purely research or offline use cases.
You’ll join a newly formed data science team, focusing on computer vision and image-based modelling. The role is highly hands-on, working across the full lifecycle, from data and model development through to deployment, collaborating closely with software and engineering teams.
What You’ll Be Doing
- Developing and training computer vision models for detection and classification
- Working with image and video data from real-world environments
- Building and optimising object detection pipelines
- Handling large and complex datasets, including labelling and augmentation
- Improving performance for challenging scenarios (e.g. small or distant objects)
- Collaborating with engineers to deploy models into production systems
What They’re Looking For
- Strong experience in computer vision and deep learning
- Hands-on experience with frameworks such as PyTorch or TensorFlow
- Experience with object detection models (e.g. YOLO, Faster R-CNN, etc.)
- Strong Python programming skills
- Experience working with real-world image or video data
Nice to Have
- Experience with OpenCV or traditional image processing techniques
- Experience working with sensor or camera-based systems
- Exposure to real-time or edge deployment
- Experience with multi-spectral or infrared imagery
If this role looks a good fit, or you’d like to find out more about the business and their products, please apply directly. You must be based in the UK to be eligible for this role.
Computer Vision Data Scientist in Reading employer: Technify Talent Limited
Join a forward-thinking technology company that champions innovation and collaboration in the field of AI and computer vision. With a strong emphasis on employee growth, you will have the opportunity to work on cutting-edge projects that make a real-world impact, all while enjoying a supportive remote work culture that values your contributions. The company offers competitive compensation, including bonuses, and fosters an environment where your skills can flourish through hands-on experience and teamwork.
StudySmarter Expert Advice🤫
We think this is how you could land Computer Vision Data Scientist in Reading
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with fellow data scientists. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your computer vision projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge, especially around frameworks like PyTorch or TensorFlow. Practice explaining your past projects and how you tackled challenges in real-world scenarios.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be perfect for you. Plus, applying directly shows your enthusiasm and makes it easier for us to spot your application.
We think you need these skills to ace Computer Vision Data Scientist in Reading
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in computer vision and deep learning. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or frameworks like PyTorch or TensorFlow.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about computer vision and how your background makes you a great fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Real-World Experience:Since we’re all about applying models in live environments, make sure to mention any hands-on experience you have with real-world image or video data. Talk about the challenges you faced and how you overcame them!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing applications come in through our own platform!
How to prepare for a job interview at Technify Talent Limited
✨Know Your Tech
Make sure you brush up on your knowledge of computer vision and deep learning frameworks like PyTorch and TensorFlow. Be ready to discuss specific projects where you've applied these technologies, especially in real-world scenarios.
✨Showcase Your Projects
Prepare to talk about your hands-on experience with object detection models like YOLO or Faster R-CNN. Bring examples of how you've developed and optimised pipelines, and be ready to explain the challenges you faced and how you overcame them.
✨Data Handling Skills
Since you'll be working with large datasets, be prepared to discuss your approach to data labelling and augmentation. Highlight any experience you have with complex datasets and how you improved model performance in challenging situations.
✨Collaboration is Key
This role involves working closely with engineers, so be ready to share examples of successful collaborations. Discuss how you’ve worked with cross-functional teams to deploy models into production systems and the impact it had on the project.
