• Data-driven PoCs, prototypes, pilots and running systems.
• Scientific Data Science and Machine Learning Research.
• Project Management.
• Studying New Data Sources.
• Visualization and Reports.
• Building o¬shore teams.
• Building in-house teams.
• Crowd-sourcing – plan and organize data science crowdsourcing projects (e.g. Kaggle, hackathons, etc).
• Big Data, Data Science, AI and Machine Learning Consulting.
• Cloud Engineering and Infrastructure Product Analytics
• Digital Strategy.
• Data Management Governance
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