Data Science Team Leader

Published on Jul 20, 2021

Intersog

An Outstaff company with office in Kyiv, Ukraine.

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Experience:

3-5 years

Job Type:

Full-Time

Remote

Specializations:

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

Must-have

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.