Samuel Mandillah

Background

About Samuel Mandillah

I am a data scientist and business analyst with extensive experience across various domains, including insurance, technology, venture capital, customer experience and content development. I am passionate about leveraging data to drive business growth and solve complex challenges. I specialize in developing advanced machine learning models and scalable data solutions that optimize decision-making processes and data storytelling. With a proven track record, I have had the privilege of working with diverse clientele and cross-functional teams across the globe, including the US, India, Germany, Ghana, Nigeria, and my current base in East Africa. Expertise Highlights: Machine Learning & AI: Proficient in developing predictive models, recommendation systems, and natural language processing (NLP), anomaly detection algorithms and fraud detection models and applications to extract insights, flag anomalies and enhance user experiences. Big Data Analytics: Skilled in extracting, cleaning, and analyzing large datasets using tools such as Python, R, SQL. Statistical Analysis: Experienced in applying statistical techniques to interpret data trends, validate hypotheses, and make data-driven recommendations. Data Visualization: Proficient in creating compelling visualizations and dashboards using tools like PowerBI, Logi Studio, Looker and matplotlib to communicate complex findings to stakeholders. Cross-functional Collaboration: My expertise extends beyond technical skills; I excel in project management, leadership, and problem-solving. Proven ability to collaborate with multidisciplinary teams to understand business objectives and translate them into actionable insights. Key Achievements: Led a team to develop a customer churn prediction model, resulting in a 15% reduction in customer attrition and 31% in annual revenue growth. Implemented a real-time claim fraud detection system, reducing fraudulent claim payouts by 21% and saving the company 214K annually. Published research papers in leading publications on topics such as AI for customer experience, deep learning and anomaly detection, showcasing thought leadership in the field. Build anomaly detection algorithms that helped in surfacing the most recurrent data issues, their causes and facilitated the cleanup process leading to trusted data and reports. In addition to this I implemented a company wide data quality framework that ensures the right standards are adhered to when handling data.