Responsibilities

• Create, document and develop architectural solutions;
• Creating and analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability;
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world;
• Verifying data quality, and/or ensuring it via data cleaning;
• Supervising the data acquisition process if more data is needed;
• Finding available datasets online that could be used for training;
• Defining validation strategies;
• Defining the preprocessing or feature engineering to be done on a given dataset;
• Defining data augmentation pipelines;
• Training models and tuning their hyperparameters;
• Analyzing the errors of the model and designing strategies to overcome them.

Requirements

• Proficiency with a deep learning framework such as PyTorch/Tensorflow;
• Proficiency with data science stack such as NumPy, Scikit-learn, SciPy, Pandas, Matplotlib;
• Proficiency with NLP (Bag of Words, TF-IDF, Word2Vec) and basic libraries for NLP such as SpaCy, Gensim, FastText;
• Expertise in visualizing and manipulating big datasets;
• Proficiency with API development with FastAPI/Flask/Django;
• Familiarity with deep learning NLP modules BERT, GPT;
• Proficiency with SQL;
• Ability to select hardware to run an ML model with the required latency.

We offer

• Advanced health insurance
• Covered trainings and certification
• Flexibility in working time
• Corporate social activities
• Snacks in the office
• Other benefits