Data Science & AI

Знання математичної статистики та теорії ймовірності
Знання Python/R, SQL
Рівень володіння англійською мовою не нижче pre-intermediate
Вища освіта у галузі прикладної математики, статистики, економічної кібернетики, програмування
Досвід побудови моделей машинного навчання
Досвід роботи у фінансовій сфері

• Have a degree within software engineering or similar field (e.g. computer science and programming) and a strong will to continuously develop your engineering skillset
• Python, spark, azure cloud experience
• Recommender system experience will be highly meriting
• love coding and would like to deploy software engineering practice into machine learning projects
• have 4+ years working experience in developing machine learning products
• have experience with handling high volume heterogeneous data and good understanding about data storage and data structure would be a big plus
• decision oriented
• have experience in agile environment, team collaboration, data-driven development, reliable and responsible experimentation
Tools/software:
• Databricks
• Azure
• MLflow

Required language skills:
• English

— 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 working in a Linux environment
— Basic Git knowledge: creating and merging branches, cherry-picking commits, examining the diff between two hashes. More advanced Git usage is a plus, particularly: development on feature-specific branches, squashing and rebasing commits, and breaking large changes into small, easily-digestible diffs
— Experience with SQL
— Understanding of algorithm complexity and performance implications
— Knowledge of classical data structures and algorithms

— MS or higher in the following areas: Statistics and Mathematics
— At least 3-5 years of professional industry experience, in addition to your academic experience
— Outstanding quantitative analytical ability
— Able to take less than precise business requirements and translate them into logic problems that you enjoy solving
— Independent and creative approach to problem-solving
— Excellent written and verbal communication skills, with prior experience explaining assumptions, conclusions, and methodology to both internal and external customers
— In-depth knowledge of Statistics/Probability/Machine Learning
— General Statistical concepts such as hypothesis testing, estimation, inference
— Supervised and unsupervised statistical techniques such as regression (linear / logistic), time series analysis, clustering
— Machine Learning foundations such as bias/variance trade-off, regularization, dimension reduction
— Real world experience with popular machine Learning algorithms such as Random Forest, Boosting, SVMs
— Strong programming experience in Python and one of the following: Scala/Java, R
— Understanding of algorithm complexity and performance implications
— Knowledge of data structures and algorithms
— Good knowledge of Knowledge Engineering
— Good knowledge of Graph technology, Knowledge Graphs, Graph Databases and Ontologies
— Experience with SQL

Strong research skills, you know how to find required information pretty quickly and know how to organize it the right way
Details oriented
Ability to perform repetitive research tasks
Excel - advanced level
Strong analytical skills, experience with creating dashboards
Intermediate or higher level of English
Proactive attitude and high self-organization
Will be an advantage
Experience with lead generation tools for social networks and email outbound campaigns
Interest and relevant industry knowledge in healthcare domain
Experience with HubSpot or other CRM systems
Experience with such platforms as LinkedIn, CrunchBase, LinkedIn’s Sales Navigator

• Порядність в загальному розумнні цього слова
• Ми використовуємо R, але не проти Python

Strong knowledge of ML (supervised, unsupervised, semi-supervised learning);
Strong knowledge of Python (pandas, scikit-learn, numpy and others);
Strong knowledge of SQL;
Technical English;
Knowledge sharing abilities.

• Experience in commercial development in Rust
• Experience in participating in open-source projects
• Experience in managing a development team
• Strong English communication and presentation skills

3+ years industrial development experience with Python
Experience with asyncio, numpy, scipy, pandas, Tornado
Experience with cython and numeric code optimization with cython
Experience with Qt GUI development in python
Experience with git, google compute cloud, docker

● Python experience
● Prior experience with machine learning or computer vision is a big plus, but may not be strictly necessary if you are able to learn fast and understand relevant technical documentation.