Data Science,

Data Science,

Luxoft
Not Specified
Not Specified

Job Description


Description
Required Experience (Indicate nature and extent of work experience including minimum number of years required.)
Responsibilities

    . Design data modelling processes to create algorithms and predictive models and perform custom analysis
    . Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
    . Write computer programs and analyse large datasets to uncover answers to complex problems.
    . Need to be comfortable writing code working in a variety of languages such as Java, R, Python, and SQL.
    . Connect with stakeholders to gain a full understanding of the problems they're looking to solve. Look beyond what's on the surface to discover patterns and solutions within the data.
    . Communicate across a diverse audience across all levels of an organization

Skills
Must have

    . Strong hands-on experience in Python
    . Must have solid understanding on BERT model, and be able to unfreeze different layer of BERT and re-train it.
    . Must have good experience in Named Entity Recognition.
    . Candidate should have participated Kaggle challenge and achieved good rank.
    . Good knowledge in mathematics modelling, statistics, probabilities, time series data analytical skills.
    . Understanding on Machine Learning technics, Neural Network, Linear regression, sentiment analysis, Clustering, anomaly detection, NLP, chatbot, Topic Modelling, Entity Recognition.
    . Experience in UI presentation, Tableau, Power BI, R Shiny, orD3.js
    . Strong knowledge in NoSQL (MongoDB) and SQL (Oracle, MySQL, MariaDB)
    . Familiar with Linux operation system, and experience with GPU environment.NoSQL, D3.js, Phyton, Chatbots, Clustering, Natural Language Processing (NLP), Microsoft Power BI, MariaDB, Oracle, MySQL, Linux, Data Analysis

Nice to have

    Good knowledge in mathematics modelling, statistics, probabilities

Languages
English: C2 Proficient
Seniority
Regular

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