About Me
Analytically rigorous Data Scientist with a Post-Graduate Diploma in Predictive Analytics (Conestoga College, Canada) and a B.Sc. in Mathematics (University of Kerala). Skilled in Python, SQL, machine learning, statistic…
Analytically rigorous Data Scientist with a Post-Graduate Diploma in Predictive Analytics (Conestoga College, Canada) and a B.Sc. in Mathematics (University of Kerala). Skilled in Python, SQL, machine learning, statistical modelling, data wrangling, exploratory data analysis (EDA), and predictive modelling using Scikit-learn, Pandas, NumPy, Matplotlib, and Seaborn. Experienced in building end-to-end data science pipelines — from data collection and cleaning through feature engineering, model training, cross-validation, and data visualisation — with a focus on translating complex data into actionable business insights. Complemented by a Diploma in Python Full-Stack Development and a disciplined professional background in quality-critical international manufacturing environments supplying Toyota and Honda. Currently based in Dubai on a UAE Visit Visa and available to join immediately with no notice period.
Experience
Robotic Welding Operations Associate
<p>Operated and monitored robotic welding systems used in the production of automotive components for Toyota and Honda.</p><p> Applied structured root-cause analysis and systematic defect investigation to resolve production anomalies.</p><p> Collaborated cross-functionally with engineering, quality control, and production planning teams in a diverse international workplace, developing strong stakeholder communication and cross-cultural teamwork skills.</p>
CNC Wire form Operator
<p>Operated precision CNC wire form machines to produce high-tolerance metal components.</p><p> Monitored real-time machine parameters and interpreted technical blueprints.</p><p> Promoted from Stamping Associate to CNC Operator within 3 months on merit.</p>
PROJECTS
Customer Churn Prediction | Machine Learning
<ul><li>Developed an end-to-end machine learning model to predict telecom customer churn using demographic, service usage, and billing data.</li><li>Built a Logistic Regression classifier achieving 82% accuracy; applied threshold tuning to improve churn recall for high-risk customers by 18%.</li><li>Provided data-driven retention recommendations targeting high-risk segments with contract upgrade and loyalty offers.</li></ul><p></p>