Data Scientist

• RTB( DSP(DemandSide Platform)
  

• WiFi KPI
  

• In RTB (realtime bidding) one of the main mechanisms for internet advertising we mainly design and develop machine learning models for DSP (DemandSide Platform) that optimize the costeffectiveness of advertisers and measure their effectiveness.
  

• We will use causal inference technology to measure the effectiveness of new features and measures for apps that facilitate free WiFi connection and improve the KPIs of our services by establishing a system for datadriven management decisions.
  

• We will apply data analysis and machine learning technology to support cryptocurrency trading fraud detection etc.
  

• While working on projects we will all spend a certain amount of time researching cuttingedge machine learning methods and new machine learning applications
  
• In addition members selected every quarter will focus on research and development
  

Requirements

• Transformer GNN MLP / Gradient Boost Tree LR Random Forest ExtraTree Ada Boost XGBoost LightGBM PCA FPGrowth Word2Vec Doc2Vec HMM
  

• t F MCMC
  

• Python PyData numpy scipy pandas Streamlit
  
• PyTorch TensorFlow LangChain Spark PySpark
  

• Google Cloud GCS BigQuery VertexAI Dataflow
  
• AWS S3 Athena EMR/Serverless StepFunction SageMaker Bedrock
  
• MySQL MariaDB Percona Server PostgreSQL Galera Cluster Oracle Hive Hadoop/HDFS
  
• ConoHa GPU
  
• LLM
  
• OpenAI API Llama3 LangChain HuggingFace
  

• Atlassian Jira Confluence Trello
  
• VS Code PyCharm Jupyter
  
• GitHub Copilot
  
• Tableau Looker Studio metabase
  
• ChatGPT Gemini Claude
  

• scrum
  

• Transformer series (largescale language models etc. graph neural network (GNN) multilayer perceptron (MLP) ensemble learning/gradient boosting (Gradient Boost Tree LR Random Forest ExtraTree Ada Boost XGBoost LightGBM) PCA FPGrowth Word2Vec Doc2Vec collaborative filtering Bayesian inference HMM model (hidden Markov model)
  

• ttest chisquare test Ftest binomial test KolmogorovSmirnov test ShapiroWilk test sampling (MCMC bootstrap method etc. analysis of variance causal inference (difference of differences method etc.
  

• Python PyData (numpy scipy pandas etc. Streamlit
  
• PyTorch TensorFlow LangChain Spark (PySpark)
  

• Google Cloud (GCS BigQuery VertexAI Dataflow etc.
  
• AWS (S3 Athena EMR/Serverless StepFunction SageMaker Bedrock etc.
  
• MySQL MariaDB Percona Server PostgreSQL Galera Cluster Oracle Hive Hadoop/HDFS
  
• ConoHa (GPU server)
  
• Largescale language model (LLM) related
  
• OpenAI API Llama3 LangChain HuggingFace
  

• Atlassian (Jira Confluence) Trello
  
• VS Code PyCharm Jupyter
  
• GitHub (Copilot)
  
• Tableau Looker Studio metabase
  
• ChatGPT Gemini Claude
  

• Agile development (scrumbased)
  

• RTB( DSP(Demand-Side Platform)
  

• WiFi KPI
  

• In RTB (real-time bidding), one of the main mechanisms for internet advertising, we mainly design and develop machine learning models for DSP (Demand-Side Platform) that optimize the cost-effectiveness of advertisers, and measure their effectiveness.
  

• We will use causal inference technology to measure the effectiveness of new features and measures for apps that facilitate free WiFi connection, and improve the KPIs of our services by establishing a system for data-driven management decisions.
  

• We will apply data analysis and machine learning technology to support cryptocurrency trading, fraud detection, etc.
  

• While working on projects, we will all spend a certain amount of time researching cutting-edge machine learning methods and new machine learning applications
  
• In addition, members selected every quarter will focus on research and development
  

Requirements

• Transformer GNN MLP / Gradient Boost Tree + LR, Random Forest, ExtraTree , Ada Boost, XGBoost, LightGBM PCA FP-Growth Word2Vec Doc2Vec HMM
  

• t F MCMC,
  

• Python PyData numpy scipy pandas Streamlit
  
• PyTorch TensorFlow LangChain Spark PySpark
  

• Google Cloud GCS BigQuery VertexAI Dataflow
  
• AWS S3 Athena EMR/Serverless StepFunction SageMaker Bedrock
  
• MySQL MariaDB Percona Server PostgreSQL Galera Cluster Oracle Hive Hadoop/HDFS
  
• ConoHa GPU
  
• LLM
  
• OpenAI API Llama3 LangChain HuggingFace
  

• Atlassian Jira Confluence Trello
  
• VS Code PyCharm Jupyter
  
• GitHub Copilot
  
• Tableau Looker Studio metabase
  
• ChatGPT Gemini Claude
  

• scrum
  

• Transformer series (large-scale language models, etc., graph neural network (GNN), multi-layer perceptron (MLP), ensemble learning/gradient boosting (Gradient Boost Tree + LR, Random Forest, ExtraTree, Ada Boost, XGBoost, LightGBM), PCA, FP-Growth, Word2Vec, Doc2Vec, collaborative filtering, Bayesian inference, HMM model (hidden Markov model)
  

• t-test, chi-square test, F-test, binomial test, Kolmogorov-Smirnov test, Shapiro-Wilk test, sampling (MCMC, bootstrap method, etc., analysis of variance, causal inference (difference of differences method, etc.
  

• Python, PyData (numpy, scipy, pandas, etc., Streamlit
  
• PyTorch, TensorFlow, LangChain, Spark (PySpark)
  

• Google Cloud (GCS, BigQuery, VertexAI, Dataflow, etc.
  
• AWS (S3, Athena, EMR/Serverless, StepFunction, SageMaker, Bedrock, etc.
  
• MySQL, MariaDB, Percona Server, PostgreSQL, Galera Cluster, Oracle, Hive, Hadoop/HDFS
  
• ConoHa (GPU server)
  
• Large-scale language model (LLM) related
  
• OpenAI API, Llama3, LangChain, HuggingFace
  

• Atlassian (Jira, Confluence), Trello
  
• VS Code, PyCharm, Jupyter
  
• GitHub (Copilot)
  
• Tableau, Looker Studio, metabase
  
• ChatGPT, Gemini, Claude
  

• Agile development (scrum-based)