Data Science: ML/DL Predictive Analytics/Portfolio Optimization

Tasks

• 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

Experience

• 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.

TECHNICALSKILLS:

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