Data Scientist

Experience: 1-3 years
Minsk, full-time

Domains:

сommon knowledges: Machine Learning, Deep Learning, NLP, recommender systems, data manipulations with big data (preferably in GCP), work with DWH;
OS: knowledges of Linux user, service manipulations, BASH scripting;
knowledges and skills in SQL and NoSQL (preferably PostgreSQL, BigQuery, Redis, MongoDB);
development: Python, scikit-learn Tensorflow, Keras, Pandas, NumPy, XGBoost, CatBoost, deploy models to prod with RESTful API for Flask;
english level: Intermediate or higher.

building models based on classical algorithms such as KNN, regression, decision trees etc.;
building and finetuning of neural networks models;
building and finetuning of neural networks models;
deployment models to production;
A/B testing, data analysis, interpretation and visualization;
research & reporting for business needs;
data quality control;
active participation in the development of solutions: creating and implementation of businesslogic, participation in architecture design of ETL-pipelines and DWH;
communication with the customer, understanding of the customer’s problems from a business perspective, decomposition of the problem/requirements for subtasks;
knowledge sharing and internal trainings.

familiar with Docker, Jenkins (simple jobs and pipelines);
familiar with visualization & dashboarding using Google Data Studio;
familiar with Terraform;
have Golang knowledge/experience;
have experience in Kaggle competitions;
have certificates from Yandex or Google courses in Coursera (DS or Big Data Specializations);
familiar with ETL Tools: Spark, Apache Beam;
familiar with Kubeflow or other reproducible model deployment tools;
knowledges in Google Cloud Platform: common architecture understanding, cloud load balancing, managedgroups, GCS, GCE, PubSub, Cloud Dataflow, Dataproc, BigQuery, Cloud Source repositories (Git), Cloud Build & triggers, AI Platform for model deploy;
postgraduate student (have researcher’s diploma).

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