Yusuf Said

Enhanced budget allocations and supported strategic decision making which decreased costs by 5.6% using robust exploratory data analysis and tailored regression models using python. Moreover, boosted revenues by 3.2% and discovered client clusters, which optimized market penetration by deploying supervised and unsupervised machine learning models whilst explaining and collaborating with cross functional teams.

Enhanced budget allocations and supported strategic decision making which decreased costs by 5.6% using robust exploratory data analysis and tailored regression models using python.
Boosted revenues by 3.2% and discovered client clusters, which optimized market penetration by deploying supervised and unsupervised machine learning models whilst explaining and collaborating with cross functional teams.

Improved customer retention, increased conversion by 4.2% and discovered demand over time using time series analysis which developed insights on customer journeys using python and R.
Supported strategic planning for expansion of services using actionable insights that derived from complex statistical data analysis using python and optimized varied datasets with SQL queries pulled with hive Hadoop, successfully expanded by 2.3%.

Prolonged patient’s lifetime value and increased sales by 4.2% using robust machine learning classification models of health demand and clustered high risks areas to target resources.
Successively launched two new general and specialty medicine which generated 2.6% increase in profits, whilst working in a multi-disciplinary team to manage, interrogate and deploy robust reports, applications, and dashboards using supervised machine learning models and data mining techniques.

Improved operations by 2.9% due to optimizing demand in pump stations by deploying various predictive models such as RF and SVM to classify demand (high, medium, and low) using Python, SQL, and numerous others.
Successfully supported strategic planning for expansion of ten new pump stations using rigorous clustering models and complex statistical analysis in python and SQL.

Completed a Master of Science degree in Data science and modules included machine learning, quantitative data analysis and several others using various languages such as R, SQL, and Python.
Completed various projects ranging from credit default prediction to predicting UK referendum using advanced data science methodologies.
Was in various societies such as big data society and represented students as union leader for countless undergraduate students.

Improved operational challenges for thousands of users by deploying payments automation solutions using various machine learning models in python and R.
Provided actionable insights with exploratory data analysis to better understand products appropriateness and insights target regions, subsequently foreseen market entry challenges.