TEMILORUN GBOLAHAN
United Kingdom
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PROFESSIONAL PROFILE
A results-driven Senior Data Scientist with over five years of experience in leveraging advanced analytics, machine
learning, deep learning and natural language processing to drive business innovation and strategic decision-making.
Proven track record of leading successful data science projects across diverse sectors, enhancing forecasting
accuracy, customer satisfaction, and operational efficiency. Technically proficient in Python, SQL, and cloud
platforms like Azure, with expertise in MLOps and Agile methodologies. Adept at transforming complex datasets
into actionable insights and collaborating effectively with both technical and non-technical stakeholders.
TECHNICAL SKILLS
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Programming skills in Python, Flask, Streamlit and JavaScript
Knowledge of cloud infrastructure: Azure, AWS, Paperspace, Heroku and Render
Data visualisation with Python (Seaborn, Matplotlib and more), PowerBI, and Tableau
Proficient in SQL (MySQL, Postgres, and Microsoft Server) and NoSQL (MongoDB)
Knowledge of MLOps tools such as MLFlow, Docker, DVC, Git, GitHub Actions, Evidently AI, Python ML Libraries
Familiar with Big Data tools (Spark, Databricks)
Other skills: Hypothesis Testing, A/B Testing, Agile, CRISP-DM, ETL, Data Governance, MS Excel, Project
Management, Code Documentation, Research, Stakeholder Management, Presentation & Communication Skill
PROFESSIONAL EXPERIENCE
Senior Data Scientist, BrytaHub Innovations Limited
Jan 2023 – Present
● Conducted comprehensive Exploratory Data Analysis (EDA) to uncover critical business insights, contributing to
a strategic shift that increased revenue by 10%.
● Led the development and deployment of predictive models using machine learning algorithms, resulting in a
20% increase in sales forecasting accuracy, leveraging CRISP-DM and Agile (iterative) methodology.
● Designed and implemented NLP models to analyse customer feedback, leading to a 15% improvement in
customer satisfaction scores by identifying and addressing common pain points.
● Engineered and optimized data pipelines for real-time data processing, reducing data latency by 30% and
enhancing decision-making speed for business operations.
● Developed machine learning models for churn analysis and customer segmentation, enabling customer
retention and targeted marketing campaigns that boosted conversion rates by 25%.
● Designed and deployed interactive data visualization dashboards using Tableau and Power BI, providing realtime business insights to stakeholders and enhancing data-driven decision-making.
● Collaborated with cross-functional teams to integrate data-driven solutions into business processes, leading to
a 15% improvement in operational efficiency.
● Conducted hypothesis testing and A/B testing to validate data-driven initiatives, leading to a 12% increase in
user engagement and improved feature adoption.
Data Scientist, Federal Ministry of Environment
Jan 2020 – Jan 2023
● Engineered and optimized data pipelines to streamline the collection, cleaning, and processing of
environmental data from multiple sources.
● Led the development of statistical and predictive models to forecast environmental impacts of proposed
projects, improving assessment accuracy by 25%.
● Deployed NLP-based applications for sector categorisation and review of EIA projects, respectively, reducing
error in project classification by 90% and reducing time invested in reviewing reports by 50%.
● Designed Environmental Impact Dashboards providing real-time data insights to stakeholders, enhancing
transparency, and facilitating strategic data-driven decision-making.
● Performed advanced statistical analyses to identify trends and correlations in environmental data.
● Worked closely with cross-functional teams, including ecologists, hydrologists, and policy makers, to integrate
data-driven insights into environmental impact assessments.
EDUCATION
M.Sc in Artificial Intelligence and Data Science, University of Hull, UK
B.Tech in Pure and Applied Physics, LAUTECH, Ogbomoso - Nigeria
Jan 2023– Jan 2024
Oct 2004– Dec 2009
