Machine Learning & AI
Forecasting models for one of the world's largest supply chains, validated through rigorous backtesting, alongside hands-on work with NLP, transformers, and generative AI and LLMs.
Çağla Akın
Data Science & AI · Music Technologies · DJ
Hello world! My name is Çağla, pronounced 'chaala'. I am a creative soul & analytical mind sitting at the intersection of music and data.
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About
I'm a Finance Manager at Amazon whose work sits squarely at the intersection of data science, product and finance. Over the past seven years, I've built forecasting, simulation and causal inference models behind some of Amazon's biggest supply chain decisions, first in Seattle and now in London.
- Based in London, UK
- Focus Data science · AI · Product · Finance · Music & Entertainment Tech
- Education Imperial College · Carnegie Mellon University (PhD research) · Bilkent · Bocconi · AllWomen Tech
- Tools Python · SQL · XGBoost · NLP & LLMs · AWS SageMaker · Tableau
Among the highlights at Amazon: a simulation model, built in two weeks, that guided Amazon's pandemic response and helped reset delivery promises worldwide; the forecasting model behind one-day delivery expansion in North America; and Amazon's first causal inference model quantifying the cost of labor forecast error. Additionally, I've designed and run A/B tests, engineered ETL pipelines using SQL and Python, served as acting Senior Product Manager, and built global datasets that standardized and automated operations finance workflows.
Alongside my professional experience, I completed executive education in Data Science at Imperial College Business School and have built hands-on projects across machine learning, NLP and AI applications. I'm also a Tech Nation-endorsed Exceptional Talent in Digital Technology.
Before Amazon, I trained in causal inference and econometrics during my PhD studies at Carnegie Mellon University, building methodologies to extract meaningful relationships from large datasets. My research earned the American Accounting Association's Best Doctoral Student Paper Award. Prior to that, I worked as an M&A Advisory Associate at Deloitte Consulting.
Beyond my professional career, I am committed to mentoring young professionals and tech enthusiasts, helping them navigate careers in tech. I'm also a DJ and music producer; curating radio, club and hospitality experiences across London venues and stations, with working knowledge of independent labels, artist communities and music discovery culture.
I hold UK Indefinite Leave to Remain (via Global Talent route) and do not require sponsorship.
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Expertise & Highlights
Causal Inference
PhD-trained in econometrics. Pioneered Amazon's first causal inference model on the financial cost of forecast error, co-designed with Core AI economists.
Simulation & Crisis Response
Built the supply chain simulation that became the single source of truth behind Amazon's pandemic-era delivery promises, informing decisions at the most senior level.
Data Products & Infrastructure
Architected end-to-end data infrastructure for anomaly detection and financial analytics, turning disparate sources into curated, model-ready data libraries.
Product & Experimentation
Designed A/B tests that quantified the cost of capacity shortfalls, and shipped global data products that removed heavy manual workloads for operations teams.
Finance Strategy
Owned the financial strategy of a multi-billion-dollar inventory portfolio across Europe and Japan, translating models into bottom-line impact.
Featured project
Spotify Hit Predictor
An XGBoost model trained on ~170K tracks to predict what makes a song popular, deployed to the cloud as a live app. My Imperial College capstone, and the place where my two worlds meet.
I'm now looking to take my career in data science further.
Download my resume ↓
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Music
After dark, you'll find me behind the decks as Sunrise Fiancée.
As a DJ and music producer with six months of training at the London School of Electronic Music, I curate radio, club and hospitality experiences across London venues and stations, with a working knowledge of independent labels, artist communities and music discovery culture.
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