Machine Learning Project Manager


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Your mission

During and before ML projects you will
  • meet with potential customers, understand their needs, frame projects, write offers, etc.
  • take responsibility for reaching business goals,
  • enable a smooth flow of communication in between the project's stakeholders (e.g. client business sponsor, client IT, our ML Scientists),
  • escalate issues, if necessary.

Generally, if you are interested, you may also
  • do general business development (should we expand our services?, invest more in PR?, improve internal processes?, etc.),
  • represent dida by, for instance, speaking at conferences, external company events, webinars,
  • do some of the machine learning/data science work yourself.

Your profile

You have
  • a university degree in physics, business informatics, statistics or similar,
  • at least 3 years of professional experience & managed at least 3 IT projects (bonus ML projects) ,
  • some knowledge of machine learning and some programming experience,
  • seen IT projects fail and you have an opinion on what went wrong.

About us

dida is a machine learning software company with exciting problems for instance in computer vision and natural language processing. Our team tackles applied problems for different customers by using latest scientific advancements (especially in deep learning) and therefore believes that research oriented thinking can help solving real-world problems more efficiently.

Why us?

You will meet an interdisciplinary team of people with a solid background in mathematics and statistics. We offer flexible working hours and have a nice office with good coffee in Berlin Schöneberg. We believe in science and support our team in publishing their research results.

Find below a short description of two of our current projects.

Estimate the amount of solar panels that fit on a roof (computer vision): 
Given a satellite picture and a ground image of a house, automatically detect certain elements of a roof (including obstacles, dormers etc.) in order to find out how many solar panels fit on it. This involves inferring 3d information from 2d pictures in order to infer the roof pitch.

Detect, classify and suggest legal effectiveness of text paragraphs (NLP): 
Automatically go through thousands of legal documents with the goal to classify dedicated paragraphs and check their legal effectiveness. This involves converting scans to text, coming up with a labelling scheme (problem modelling), and detecting different paragraphs automatically, before tackling the inference task.