Data Scientest

Welfare

• Attractive remuneration with competitive income and 6 months/year salaries/ efficient production and business.
• A welfare package of $2000 per year including Tet bonus, holiday bonus and resort.
• Being a colleague of a team of leading technology experts in fields such as: DPI, BigData, Machine Learning, AI, Data Mining, Speech Recognition, Virtual Assistant…
• Improve capacity through intensive training courses, study support and professional certification exams.
• Consider raising the minimum salary once a year. Access to attractive promotion opportunities.
• 12 days holiday, 12 vacation days and 3 annual vacation days.
• Always enjoy to the fullest full benefits of social insurance, health insurance, unemployment insurance and statutory leave and sick leave after signing the labor contract
• Join the comprehensive health care MIC insurance package.
• Relax, spark creativity with Happy Time every day, modern workspace.
• Enjoy a delicious lunch, selected by nutritionists.
• Working in key projects of the Group with large system scale.
• Experience the multiculturalism when working with Viettel's personnel in more than 10 countries.

Job Descriptions Build, evaluate, fine tune ML models, statistics to solve project use cases according to requirements and existing solutions

Define and implement normalization methods, transform, synthesize, extract suitable data to build features for ML algorithms

Coordinate with Data Analyst to implement sample test/pilot based on model output

Build documents according to procedures, regulations and other documents to present solutions, methods of building and using model results.

Job requirements

Graduated from a regular university with good grade or higher majoring in Mathematics, Economics, Computer Science, Probability and Statistics or other quantitative disciplines

English equivalent to 650 TOEIC (No certificate required when hiring)

Knowledge of basic machine learning algorithms: decision trees, linear regression, ensemble (random forest, boosting tree), k-means, SVM...

Knowledge of popular deep learning algorithms: CNN, LSTM, RNN...

Knowledge of statistics, exploratory data analysis (EDA) )

Knowledge of programming, data structures & algorithms, graph theory

Knowledge of statistical probability, linear algebra, analysis

Skill in using one of the machine learning libraries ( scikit-learn, pyspark ML, TensorFlow, Keras)

Distributed data processing knowledge, big data processing (Hadoop, Spark) - optional

Skills to use using programming languages: R, Python

SQL skills.

ETL flow building skills