Tigran Khachatryan

• Integrated cutting-edge data science research into the product portfolio, driving the development and expansion of data-driven products and solutions.
• Led the end-to-end development and deployment of deep learning and computer vision systems for large-scale social media analysis, processing terabytes of audiovisual data and billions of metric streams in production.
• Developed advanced algorithms and scalable analytical systems for social media intelligence, including content classification, trend detection, user behavior analysis, complex network analysis, and time-series forecasting.
• Contributed to a 3× growth in company revenue to €1.5M and the expansion of the product portfolio, helping establish the company in a competitive position within the market.

• Led CHAOS’s Data Science R&D team and defined the long-term R&D strategy, aligning research initiatives with product and business objectives to strengthen the company’s competitive position.
• Owned the end-to-end development of large-scale machine learning solutions, from ETL and modelling to deployment and monitoring, integrating advanced Data Science research into the product portfolio.
• Developed state-of-the-art spatial and temporal modelling solutions, including synthetic spatial data generation, nationwide demographic forecasting across multiple EU countries, and agent-based smart mobility simulations.
• Led the migration to the H3 Spatial Indexing System, achieving a 10× improvement in data retrieval speed while significantly reducing storage costs; developed synthetic datasets valued at over €3M.

• Delivered end-to-end Data Science consulting through the Toptal network, advising clients on high-impact business problems and translating strategic requirements into statistical, machine learning, forecasting, and recommendation solutions.
• Led the design and delivery of production-grade data and machine learning solutions, spanning data acquisition, data engineering, feature engineering, model development, evaluation, deployment, and monitoring.
• Developed tailored predictive and analytical solutions across 10+ client engagements, including multi-year projects, covering a multitude of applied machine learning use cases.
• Delivered 10+ client projects, generating measurable business impact and €5M+ in value through increased revenue, cost savings, and data-driven decision-making.

Conducted data-driven research within the Aalto ADS research group, examining how topological structures affect information encoding and decoding in spatially embedded complex networks. Utilized geometric deep learning with Graph Neural Networks to investigate how topological invariants in source networks can reveal structural patterns within target networks.

Dropped out in 2022 to help startups solve real-world problems with AI and machine learning.

The master’s thesis focused on applying data-driven spatial modelling techniques. It studied the shift from geometry-driven to big data-driven spatial analysis and how spatial data can be formally represented with graph encodings. The thesis laid the foundation for subsequent doctoral research on graph-based learning in spatial networks.

Volunteered with the Finnish Red Cross, taking on leadership responsibilities in youth-focused activities and supporting the planning and coordination of community initiatives.