Jephtah Paul

About

Experienced data scientist/data analyst leveraging advanced analytical techniques to extract insights and drive business growth. Skilled in machine learning, statistical analysis, time series forecasting, data visualization, and automation using Python, R, SQL, Power BI, AWS, and Excel. Proven ability to deliver end-to-end analytics solutions—from problem formulation through deployment—while collaborating cross-functionally to translate business needs into actionable models that improve sales performance, operational efficiency, risk management, and decision-making.

Experience

• Data Analyst · Apogee (An HP Company)07-01-2024

  Delivered actionable insights and advanced analytics to support sales performance, client services, and operational ediciency within a leading Managed IT provider. Designed and implemented data-driven solutions to optimise reporting, identify revenue opportunities, and improve decision-making across sales, account management, and service delivery teams. Key Achievements: • Improved sales forecasting accuracy by 18% through predictive models in Python, enabling better pipeline management and resource planning for account managers. • Developed and automated Power BI dashboards for monitoring sales KPIs, client contracts, and service performance, reducing reporting time by 30% and improving executive visibility. • Increased win rates by 12% by building lead scoring and customer segmentation models that guided targeted sales campaigns. • Enhanced account retention by analysing contract usage patterns, identifying at-risk clients, and providing proactive recommendations to the client success team. • Presented data-driven insights to senior leadership, directly influencing pricing strategies and cross-selling initiatives across managed print, cloud, and IT services.

• Data Scientist · Bird USA04-01-2023 – 06-30-2024

  Develop and implement advanced machine learning algorithms and NLP techniques to extract valuable insights from large datasets. Collaborate with cross-functional teams to identify business needs and translate them into data-driven solutions. Design and optimize predictive models to support decision-making processes and improve operational ediciencies. Key Achievements: • Innovated data interpretation approaches, enabling the identification of previously unnoticed patterns and trends. Achieved a 25% reduction in loan default rates by implementing sentiment analysis in predictive models. • Revolutionized predictive modeling methodologies, resulting in substantial improvements in loan default prediction accuracy. • Boosted customer engagement by 30% through the design and execution of dynamic data visualization projects. • Produced accurate documentation, incident reports, and performance dashboards for management review. • Developed scripts and queries to automate routine monitoring and reporting tasks.

• Data Analyst · NHS Midlands & Lancashire CSU10-01-2022 – 03-31-2023

  • Managed large volumes of sensitive patient data, ensuring compliance with GDPR. • Built and maintained Power BI dashboards for senior managers to monitor operational KPIs. • Conducted data quality control and implemented audit processes, improving reporting accuracy.

• Data Scientist · Access Bank Nigeria03-01-2018 – 12-31-2021

  Conducted large-scale data analysis to support retail and corporate banking operations across Africa, applying advanced statistical techniques to identify transaction patterns, customer behaviour, and sales performance trends. Designed and automated dashboards, reports, and predictive models to guide strategic decision-making, collaborating with sales, marketing, and risk teams to improve operational ediciency and customer engagement. Key Achievements: • Enhanced operational ediciency by automating data pipelines and reporting processes, reducing manual analysis time by 40%. • Developed a customer segmentation model that increased targeted marketing ediciency by 25%, boosting cross-selling opportunities and product uptake. • Improved sales forecasting accuracy by 15% by implementing time-series models that optimised resource allocation and campaign planning. • Strengthened risk management by building early-warning models that identified high-risk customer accounts, reducing default rates. • Improved executive decision-making by delivering interactive dashboards in Power BI and SQL, enabling real-time monitoring of KPIs across 10+ business units.

• Data Analyst Intern · Nigerian Meteorological Agency03-01-2018 – 12-31-2021

  Supported the analysis and reporting of meteorological data to improve forecasting accuracy and strengthen national weather monitoring capabilities. Worked with large climate datasets, applying statistical and analytical techniques to identify trends, validate observations, and support model-based forecasts. Collaborated with meteorologists to transform observational data into actionable insights for aviation, agriculture, and disaster preparedness stakeholders. Key Achievements: • Contributed to numerical weather prediction (NWP) edorts by preprocessing and validating observational datasets for assimilation into forecasting systems. • Performed statistical analysis of rainfall and temperature trends, supporting seasonal forecasts and climate impact assessments. • Assisted in quality control of observational data, ensuring integrity of datasets used for operational forecasts and research. • Presented analysis findings to meteorological teams and external stakeholders, strengthening communication of technical insights to non-technical audiences.

Education

• Stadordshire UniversityMSc (Distinction), Computer Science
• Federal University of Petroleum ResourcesBSc (4.21/5.0), Environmental Science

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