Data Science: ML/DL Predictive Analytics/Portfolio Optimization
Местоположение и тип занятости
- Participate in development robo - adviser model (recommender system) allowing clients to make the right decision on securities market
- Develop and implement ML automatic procedures for build & analyze client’s portfolios.
- Implement real time portfolio optimization ML algorithms with usage all acceptable scope external and internal data and utilize forward-looking risk indicators for each client, individual positions & issuers
- Ability to transform business requirements into actionable data models, prediction models and informative reporting solutions.
- Knowledge in Data Mining, Machine Learning with big structured and unstructured data.
- Life cycle models support: Data Acquisition, Preparation/Quality/Dimension reduction, feature engineering, hypothesis testing, sampling, ML modeling (supervised and unsupervised), validation and visualization.
- Skilled in performing text Analysis, data parsing.
- Deep understanding in Statistical modeling
- Hands on experience with various libraries in Python.
- Working knowledge with PostgreSQL, Hadoop.
- Hands on experience in implementing regression and classification models, recommender systems.
- Good industry knowledge, analytical and problem-solving skills and ability to work well within a team as well as an individual.
Adept at using Python (Pandas, Numpy, XGBoost, Scipy, Scikit-Learn, Keras, Seaborn, Tensorflow)
Standard analysis and modeling: Feature Selection Methods, Principal Component Analysis, Supervised and Unsupervised, Regression and Classification Techniques, Time Series analysis
Advanced modeling: Support Vector Machines, Random Forest, XGboost, NLP, Deep Learning techniques
Operating Systems: Linux base platform
Other Tools: Git/Github JIRA/Confluence