Teslim Olagoke

TESLIM OLAGOKE Data Scientist | Lagos, Nigeria | PROFESSIONAL SUMMARY:         3+ years of experience as a Data Scientist, driving user acquisition, retention, and product growth through data-driven insights. Proficient across multi-cloud environments (AWS, Azure, Google Cloud) with expertise in deploying data and ML solutions on the cloud. Strong expertise in SQL, Python (Pandas, SciPy, Jupyter), and Apache Spark for large-scale data analysis and advanced modelling. Proven track record applying predictive churn models, CLTV analysis, and customer segmentation, improving retention by 21% and boosting conversion rates by 12%. Skilled in A/B testing, multivariate testing, and statistical modelling to optimize product performance and guide business decisions Designed and deployed interactive dashboards and self-service analytics tools (Power BI, Tableau, Excel) to give stakeholders real-time visibility into KPIs and growth metrics. Leveraged Python, SQL, and Apache Spark for large-scale data analysis, predictive modelling, and automation on Azure Databricks and AWS environments. Experienced in translating product and business questions into actionable insights, partnering with product, engineering, and business teams to deliver impacts. SKILLS: Programming & Analytics: Python, SQL, Statistical Analysis, A/B Testing, Forecasting, Clustering, Machine Learning Cloud Platforms: AWS (Certified Cloud Practitioner), Azure, Google Cloud Infrastructure & Automation: Azure Data Factory, Databricks, IaC concepts (Terraform, CloudFormation exposure) Experimentation & Product Analytics: A/B testing, multivariate testing, KPI design, growth metrics tracking Data Visualization: Power BI, Tableau, Matplotlib, Seaborn, Microsoft Excel Databases: Oracle SQL, MS SQL, MySQL, PostgreSQL, Azure SQL Pool Big Data Tools: Apache Spark Collaboration & Stakeholder Management: Cross-functional alignment with Product, Engineering, Business & Marketing teams; Strategic Insight Communication CI/CD & DevOps: Pipeline automation, cloud deployment. Soft Skills: Critical Thinking, Communication, Problem-Solving, Attention to Detail, Team Collaboration, Time Management CERTIFICATIONS: AWS Certified Cloud Practitioner Credential ID: 2d9511c99b3f49af9a13f4a79e32a5a8 Microsoft Certified: Azure Data Scientist Associate Credential ID: F7CBBBFD3DC2950E Microsoft Certified: Azure AI Fundamentals Credential ID: B-DEACC589 Introducing Generative AI with AWS (Udacity) Credential ID: 272c307a-3d32-11f0-a94d-4f26aeace7d1 July, 2024 June, 2024 May, 2024 July,2025 EDUCATION: Bachelor of Engineering (Mechanical Engineering) Kwara State University, Nigeria August 2016 - November 2021 WORK EXPERIENCE: United Bank for Africa (UBA) Data Scientist July 2023 – Present I leverage Python, SQL, and Apache Spark to process, analyse, and manage large-scale datasets, building and maintaining ETL pipelines for efficient data workflows.  I design, develop, and publish interactive Power BI dashboards—visualizing churn attrition hotspots and real-time KPIs—to enable data-driven decision-making.  I develop and deploy predictive automation models (XGBoost) in Azure Databricks, engineering behavioural features (transaction volumes, frequency scores, value trends) for churn and customer segmentation analytics.  I collaborate with stakeholders to automate BI solutions and real-time churn alerting, streamlining reporting processes and enhancing customer onboarding.  I utilize Azure cloud services (Databricks notebooks, Synapse) to scale data analysis, ensure data quality, and manage transformation and cleansing workflows.  I conduct statistical modelling and machine learning analyses in Python to drive targeted marketing strategies, improve customer retention, and increase revenue. NUPAT TECHNOLOGY August 2022 – July 2023 Machine Learning Engineer        I maintained robust governance for machine learning models by implementing and enforcing processes, policies, and procedures that ensured accuracy, reproducibility, and compliance. I collaborated with finance teams to execute month-end and year-end closes, prepare financial statements, and analyse budget variances—streamlining periodic business performance reporting. I partnered with cross-functional stakeholders to monitor business performance, control expenditures and investments, and identify risks and opportunities for targeted deployment of statistical and ML solutions. I delivered in-depth analytical and strategic insights into revenue and cost centres, guiding executive decisions that improved operational efficiency and profitability. I performed variance analysis and integrated internal and external datasets to build forecasting and predictive models, enhancing the accuracy of business projections. I researched and piloted new model-building techniques and tools, continuously advancing our predictive analytics capabilities. TEACHING EXPERIENCE: Datasphere Academy, Lagos Data Science Tutor April 2024 – Present • Taught data science subjects: data analysis, machine learning, and how to present data in detailed lessons. • Helped 171 students with projects, allowing them to put into practice skills in Python, SQL, and most-used libraries such as Pandas, NumPy, and Matplotlib. • Designed and implemented engaging curricula tailored to varying skill levels, ensuring accessibility and understanding for diverse learners. • Provided advice and feedback on students' data science projects; helped them build portfolios and achieve their career objectives. • Hosted webinars on new data science tools and technologies that are important for the industry. PROJECTS: Customer Lifetime Value (CLTV)           Developed a model (BG/NBD algorithm) to calculate Customer Lifetime Value (CLV) using historical transaction data, identifying high-value customers for targeted marketing strategies. Utilized Python libraries for data preprocessing, feature engineering, and predictive modelling, leading to a 30% increase in accuracy over traditional metrics. Implemented segmentation techniques to group customers by lifetime value, enabling the business to focus on retention efforts and personalized offers. Utilized PowerBI to visualize my findings to stakeholders. Delivered actionable insights to the marketing team, improving customer retention and boosting revenue potential by aligning resources with high-value segments. Customer Segmentation Conducted a customer segmentation project using data science to analyse and group customers based on behaviour, demographics, and purchasing patterns. Applied clustering algorithms like K-means and hierarchical clustering to identify distinct customer segments. Generated actionable insights to support targeted marketing strategies and improve customer engagement. Enhanced personalization efforts and optimized resource allocation based on segment characteristics. Utilized PowerBI to visualize my findings to stakeholders. USSD Customer Retention Automation Model     Developed and deployed a predictive churn model in Azure Databricks using XGBoost also engineered behavioural features—transaction volumes, frequency scores, value trends, and engagement deltas over 3, 6, and 12 months—from a 5.6 million-customer dataset using Python, SQL, and Apache Spark. Optimized model performance by applying class weights to address data imbalance, ensuring robust churn predictions across demographic segments. Utilized Power BI to build interactive dashboards visualizing geographic attrition hotspots, temporal decay patterns, and high-risk customer personas for executive stakeholders. Delivered strategic recommendations—smart inactivity watchlists, geo-targeted re-engagement campaigns, and tailored USSD bundles—projecting a 30% intervention success rate. Others: Customer Churn Analysis, Log Reporting Monitoring Dashboard, Financial Insights Dashboard, Log Assignment Automation, Credit Monitoring Dashboard.