Senior Deep Learning Data Scientist / Machine Learner (M/F/D)
We’re a Hamburg, Germany-based start-up with the mission to democratize cancer diagnostics. We apply artificial intelligence to improve pathologists‘ ability to identify cancer quickly and accurately. Our AI solution is the first to be used in clinical histopathology routine for primary diagnostics in the USA. We have been voted as one of the top 10 German startups of 2020 by the German Entrepreneurship Price Committee.
We solve one of the most pressing challenges in modern healthcare: Demands for medical image analysis are increasing rapidly, but the number of cancer experts remains stagnant. We build support tools for visual diagnosis using state-of-the-art deep learning. Our tools help cancer experts deliver reliable diagnoses and focus on areas where human expertise is indispensable. Optimizing cancer diagnostics with machine learning is challenging and requires the brightest minds. At Mindpeak, you will work closely with a team of industry experts in deep learning, pathology and entrepreneurship.
We need you to make a difference in the world and shape the future of cancer diagnostics!
As a Senior Deep Learning Data Scientist / Machine Learner you will be part of an interdisciplinary team and become a key driver in building highly accurate image analysis systems and machine learning models for the pathologists’ clinical routine. There are big challenges in the field of digital pathology like demanding accuracy requirements and the large variability of human cells. With your expertise and the possibilities of deep learning and artificial intelligence, you will work on solutions which will take the current healthcare to the next level and help generate a true impact beyond the status quo.
- You develop prototypes for promising deep learning methods and conduct experiments that allow for meaningful conclusions in the field of medical image analysis
- You are an expert of our image data - that means you manage, pre-process and analyse our large image data-sets for the application of machine learning
- You work closely together with the other members of our data science and tech team
- In cooperation with our software developers, you implement efficient AI production systems
- Together with our product managers and users like laboratories and medical doctors, you develop new concepts and specify data requirements for our AI products
- You will be directly reporting to the CTO
- M.Sc / P.hD in Computer Science, Mathematics, Physics or similar with excellent grades
- Expert theoretical knowledge in machine learning
- Multi -year practical experience in deep learning for computer vision
- Fluency in several of: PyTorch, Tensorflow, Keras, Numpy, Pandas, Sklearn, OpenCV, Skimage,GPU-Computing (CUDA)
- Advanced skills in Python, bash, Git and linux tools
- Ability to work in a fast changing, highly dynamic work environment with changing priorities
- Curiosity to understand the medical application
- Enjoying interdisciplinary teamwork, thinking outside the box and communication with our partners
- Independent structured working style and willingness to take responsibility
- Good analytical skills and the motivation to constantly learn, mentor, and share knowledge with others along the way
- Fluent in English, additional German is a plus
- Your path to become an impactful leader into the future of AI-based cancer diagnostics
- The opportunity to make a difference in the world with your work and bring cancer diagnostics to a whole new level
- Exciting varied work in an upscaling company with new challenges everyday
- An open and creative work environment
- Flat hierarchies and quick decisions
- Exchange of knowledge and close cooperation with exceptional colleagues in a highly motivated international team in one of Germany’s leading AI startups
- Central office location with fascinating workspace view over the Hamburg harbor
How to apply
Send your application as Senior Deep Learning Data Scientist / Machine Learner (M/F/D) with your salary expectations and earliest possible starting date as email via the link below.