Data Science Team Leader

Published on Jul 20, 2021

An Outstaff company with office in Kyiv, Ukraine.

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3-5 years
Job Type:
Data Engineering
Data Science & AI

What You Will Do Responsibilities
  • Work closely with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context;
  • Provide mentorship to other ML engineers and Data Scientists in the team;
  • Be hands-on and involved with every stage of the ML product development cycle;
  • Design, extend and review ML experiments and solutions;
  • Evaluate, justify and communicate ML models’ performance to various stakeholders;
  • Write clean and tested code that can be maintained and extended by other fellow ML engineers;

What You Should Bring
  • 2+ years as Team Leader, working with Agile methodologies;
  • 5+ years of industry and research experience in developing machine learning products;
  • Excellent written and verbal communication skills, with prior experience explaining assumptions, conclusions, and methodology to both internal and external customers;
  • Ability to frame business requirements into Machine Learning problems that you enjoy solving;
  • A strong predilection for good software and the processes that make it;
  • Mathematical foundation including linear algebra, vector calculus, probability, and statistics. Experience implementing this math effectively in software (e.g. Python, NumPy);
  • Strong foundation in machine learning & deep learning concepts including supervised and unsupervised learning, transfer learning, ensembling, classification, regression, clustering, bias & variance, regularization, overfitting & underfitting, Logistic & Linear regressions, Decision Trees, MLP, RNNs;
  • Strong foundation in natural language processing concepts including: bag-of-words & TF-IDF, n-grams, word & text embedding, NER, transformers, text classification & similarity;
  • Proficiency in Python and PyData stack (NumPy, scipy, pandas, scikit-learn);
  • Fluency with popular NLP libraries (spaCy, NLTK, transformers);
  • Hands-on experience with Deep Learning frameworks such as PyTorch;
  • Experience with SQL;
  • Understanding of algorithm complexity and performance implications;
  • Knowledge of classical data structures and algorithms;
  • Upper-Intermediate level of English.

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