About Me
AI Solutions Consultant and Data Scientist building intelligent platforms and data-driven solutions
I'm an AI Solutions Consultant and Data Scientist who helps businesses harness the power of artificial intelligence to solve real-world problems. From founding AI platforms to advising organisations on their AI strategy, I bring a rigorous analytical mindset shaped by a Ph.D. in Physics and years of hands-on experience building production systems.
I specialise in designing and building intelligent platforms, multi-agent AI systems, and data-driven applications. Whether consulting on AI feasibility, architecting agentic workflows, or developing end-to-end solutions, I combine deep technical expertise with strategic thinking to deliver measurable impact.
Data Science & AI
Expert in Machine Learning, Deep Learning, and Agentic AI. Proficient with Python, PyCharm, Cursor, PySpark, SKLearn, and cutting-edge AI frameworks including OpenAI, LangGraph, and multiple LLM platforms.
Data Engineering
Extensive experience building ETL pipelines, working with SQL, PostgreSQL, MongoDB, GraphDB, and Neo4j. Skilled in cloud computing with AWS and data visualization using Tableau and Plotly.
Analytics & Research
Strong foundation in statistical analysis, quantitative modeling, and research. Ph.D. in Physics with extensive experience in particle physics research at CERN, Fermilab, and leading international collaborations.
Recent Successes
Trader Contextual Surveillance
At Credit Suisse, contributed to the design and implementation of the Trader Contextual Surveillance (TCS) process — a Foundry- and PySpark-based data analytics solution developed to monitor trading activities across the firm's global trading platforms. The system was designed to detect patterns of potential insider dealing and market manipulation by generating alerts for trades requiring further scrutiny. Over thirty distinct surveillance scenarios were implemented, covering a wide range of behaviors and market conditions. The project not only strengthened compliance and risk management capabilities but also identified potential regulatory loopholes that had not yet been formally recognized by market authorities.
Predictive Time Series Analytics
Developed a machine learning–based trading application designed to scan a broad universe of approximately 800 publicly listed companies to identify favorable trade entry conditions. The system integrates a suite of advanced algorithms trained to detect local highs and lows within stock time series data and to estimate the probability of sustained trend reversals. Its predictive engine calculates the likelihood of a 10% price movement within a ten-day horizon, enabling a systematic and data-driven approach to short-term trading opportunities.
Holistic Risk Management
At Credit Suisse, contributed to the design and implementation of the Holistic Risk Management Framework (HRMF)— a Foundry- and PySpark-based data analytics solution developed to identify, quantify, and highlight actionable risk across the organization. The framework integrated diverse datasets from multiple business and security domains to provide a comprehensive, data-driven view of the bank's risk landscape. It implemented key metrics to evaluate the effectiveness of the bank's cybersecurity programs and enabled proactive monitoring of emerging vulnerabilities and operational risks.
