Senior Strategy Intelligence Scientist & Data Engineer ACI Limited
I turn messy enterprise data into the signal that drives boardroom decisions — building data platforms, forecasting models and decision-grade dashboards across pharma, agri, consumer, retail and logistics businesses.
From the raw warehouse layer to the executive dashboard — these are the tools, methods and frameworks I lean on day to day.
Real, runnable data systems — every repo ships with tests, seeded benchmarks and honest baselines, and every number below is reproducible with a single command. Source on GitHub.
SKU-level forecasting with LightGBM: leakage-safe features, rolling-origin backtests and baselines that fight back. 38.4% WAPE vs 69.5% seasonal-naive on a 146K-row panel — a 44.7% relative improvement, reproducible with one command.
Causal uplift meta-learners (S/T/X) with Qini evaluation and profit-priced policies. Model targeting earns 2.9× the incremental profit of a blanket campaign on a simulated randomized trial.
Online anomaly detection over 216K fleet telemetry readings: 100% fault recall, 78s median detection latency, 2.4M readings/s on one core — pure stdlib, zero dependencies.
From quality engineering and commercial operations to strategy intelligence — six years of compounding work.
Building analytics and visualization solutions that translate complex operational data into executive-level strategic decisions. Driving data-led initiatives across FMCG and consumer durables business units using Power BI, Python, and statistical modelling.
Designed and maintained data pipelines for quality KPI monitoring across OQC/IPQC/IQC/FQC stages. Used Python and Power BI to surface defect trends, automate reporting to Arçelik central teams, and apply statistical process control to reduce fault rates.
Owned end-to-end sales data analysis for S&OP reporting — built dashboards tracking market penetration, equipment reliability, and customer satisfaction metrics. Translated raw commercial data into actionable insights that aligned marketing and supply chain planning.
Applied statistical process control (SPC) and FMEA-driven data analysis to monitor assembly line quality across a high-volume manufacturing environment. Built Excel and Python-based reporting that cut daily fault tracking time and surfaced root causes for supplier corrective actions. Promoted to Asst. Director within 2 years 4 months.
First exposure to operational analytics in a data-rich manufacturing environment. Tracked productivity metrics, identified efficiency bottlenecks through data, and built the cross-functional collaboration and analytical foundations that shaped the rest of my career.
M.S. in Artificial Intelligence and Data Engineering — curriculum covers Machine Learning, Deep Learning, Reinforcement Learning, Large Language Models, Data Mining & Warehousing, Systems for AI, and Ethical AI, alongside applied mathematics and statistics for data engineering.
B.Eng. in Industrial & Production Engineering (IPE) — specialized in quantitative modeling, stochastic processes, and data-driven systems engineering. Built a rigorous foundation in statistical analysis and operations research to simulate scenarios and optimize high-impact strategic outcomes.
I'm always happy to talk shop — strategy data, ML in the enterprise, or your next interesting problem. Drop a line, I read every message.