Вакансия Lead Data Scientist

21 апреля 2021    46

The Swiss product company which offers compelling digital agronomy solutions for B2B clients and large farming businesses is looking for a Head of Data Science who will be involved in the development and commercial deployment of Data Science-driven solutions for Smart Farming. You will provide leadership, training and technological support for a team of talented data scientists in a challenging and rewarding start-up environment. ​Strong commercial delivery focus.


  • Manage delivery, technical enablement and methodology adoption in tight collaboration with Gamaya software and hardware teams and business stakeholders.
  • Drive and adjust DS roadmap based on business needs, capabilities and Gamaya business strategy.
  • Support DS projects and team with senior skills and expertise. Trigger respective content deep dives to ensure the best practice approach and outstanding quality of the solution.
  • Identify gaps with respect to DS capabilities and skills. Proactive solution finding and drive respective implementation.
  • Develop, and manage the delivery of, DS project portfolio according to priorities.
  • Strong collaboration spirit in an agile team environment.
  • Establish common practice for the development of Data Science for Remote Sensing (DS4RS) methods and solutions.
  • Engage with senior business leaders to widen understanding of DS4RS potential and risks, and related business opportunities.
  • Identify relevant industry/technology trends, engage with externals partners/research.
  • Act as a role model and "go-to-person" in respect of skills and experience

Must have, technical skills:

  • programming skills in Python, with tensorflow/pytorch
  • Google cloud platform or similar cloud infrastructure, data version control tool, and dynamic data repositories implementation
  • Supervised learning methods: sparse linear regression, kernel methods, svms, neural networks, CNN, RNN, model selection
  • Unsupervised learning methods: k means, hierarchical clustering, outlier detection, gmm, density estimation, pca, ica, VAE, GAN
  • Experience with Image processing: segmentation, denoising, inpainting, super resolution.
  • Experience with time series: classification, switch point detection, forecast, anomaly detection.

Nice to have:

  • Proven publication record in international conferences and journals (e.g., NIPS, ICML, AISTATS, MICCAI, CVPR, ICPR)
  • Advanced probabilistic modeling: tensorflow_probability/pymc3, normalizing flows, multiview learning
  • Experience with preemptible training workflow in the cloud with kubernetes with gcp or similarly azure/aws
  • Computational analysis of algorithms, distributed computing with tf.distribute or similar tool
  • Experience with optimization: first order methods: gradient descent, proximal methods, stochastic gradient descent flavours, variational inference.
  • Experience with the agribusiness

Must have, experience: 

  • Degree in Computer Science, Applied Mathematics, Statistics or related quantitative fields would be preferred.
  • +/-5 years of in-depth ​commercial experience​ in statistical analysis and machine learning.
  • Previous experience in a leadership role
  • In-depth experience​ in building and deploying innovative solutions.
  • High level of analytical problem-solving skills and the ability to assimilate complex information.
  • Experience in project management and working with cross-functional and/orvirtual teams.
  • Experience with ​Remote Sensing​ is a plus.
  • Ability to take a strategic view of Data Science and Remote Sensing and understands what is required to reach key goals.
  • Ability to detect and manage different stakeholders.
  • Strong ability to structure and prioritize. Strong delivery focus.
  • Strong communication skills (conciseness, clarity), both verbal and written.
  • Excellent English language skills 


  • Work with a great team of bright scientists and engineers
  • Be a part of the sustainable future of agriculture
  • Enjoy the dynamic and international environment
  • Compensation above the market

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