Position: Data scientist
Company: Postello
Experience required: 3y
English level: intermediate

As a software engineer at Postello, you will design and develop applications that run machine learning algorithms, allowing the algorithms to be used at high volume on production servers.

Required skills

4+ years of shown hands-on experience with design, implementation, and application of ML/AI/Deep Learning and OR solutions and techniques to build models that solve real problems.
2+ years of hands-on experience in optimization modeling, simulation, and analysis with Python or Matlab.
Experience analyzing machine data (sensors, downtime log, machine states, etc) for IoT & predictive maintenance applications.
Experience applying deep learning frameworks, such as PyTorch/ Torch, TensorFlow, Keras to real-world applications that solve problems.
Knowledge of validated approaches for scale-ability, productionalizing models, and implementing machine learning applied to expansive and diverse datasets (storage GPUs, techniques for deep learning at scale).
Strong software development skills with proficiency in Python.
Experienced user of machine learning and statistical-analysis libraries, such as GraphLab Create, scikit-learn, scipy, and NLTK.
High level of autonomy and influence to remove roadblocks and deliver results (evaluate and solve complex problems involving various teams ranging from data instrumentation to analytics tool development). A proven track record for self-study and self-exploration into new tools and techniques.
Ability to explain and present analyses of machine learning concepts to a broad technical audience.

We offer

– New office;
– English language courses with a native speaker;
– Flexible approach to the schedule. The main thing for us is the result and the involvement;
– Corporate participation in sports, eco- and social projects;
– Premium level health insurance package.

Requirements

– Determination of user data to be used;
– Creation of a model for building user profiles;
– Audience clustering for ad personalization;
– Optimization of the conversion of displayed ads.