Fola Falodun

Fola Falodun

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Lagos, Nigeria
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Résumé

Jobs verified_user 0% verified
  • P
    Product Manager
    Feb 2025 - Current (1 year 6 months)
    ● Part of a small founding team building a platform to improve freelancing across Africa (currently in stealth). ● Leading user research through direct interviews to identify pain points and opportunities in the freelance ecosystem. ● Collaborating closely with co-founders on product strategy, market research, and early user validation. ● Documenting detailed user journey maps and flowcharts to inform product design and feature prioritization. ● Collaborating on MVP definition, product roadmap, and cross-functional planning in a lean, early-stage environment. ● Leveraging my background in software development to enhance collaboration with technical teams, ensuring that product features are not only technically sound but also strat
  • T
    Data Processor
    The Delivery Group
    May 2023 - Dec 2024 (1 year 8 months)
    • Managed and audited large datasets, performing validation checks and formatting for optimized import, improving postage outcomes. • Conducted data cleansing to ensure accurate address and postcode formats, adhering to Royal Mail's PAF standards. • Incorporated advanced Excel functions to streamline the preparation of data files, enhancing efficiency by 30% and ensuring compliance with encryption protocols for protecting sensitive client information in reports. Additionally, utilized PostgreSQL to spool client datasets, further enhancing data management capabilities and ensuring robust data handling practices.
  • Access Bank PLC
    Data Insight and Compliance Analyst
    Access Bank PLC
    Mar 2019 - Jun 2021 (2 years 4 months)
    • Created SQL queries to flag suspicious transactions on high-risk accounts, improving fraud detection. • Automated IMTO transaction monitoring with Excel Macros and developed Power BI dashboards for data sharing and visualization. • Applied data visualization techniques using Python to provide actionable insights to stakeholders on the XtraWins campaign project, achieving a 40% improvement in data communication and presentation. - Maintained dashboards for easy internal data sharing, ensuring simplicity and accessibility. - Implemented ETL processes and data integration strategies to ensure data accuracy and consistency. - Collaborated with cross-functional teams, including IT, Business Intelligence (BI), and Operations to
  • D
    Internal Control Officer
    Diamond Bank
    Jun 2018 - Mar 2019 (10 months)
    • Led data cleansing efforts in a team of five to ensure the accuracy of loan monitoring data. • Analyzed data using ACL Analytics to identify card issuance issues, preventing potential risks. • Improved data reporting accuracy through ad-hoc analysis using Excel. • Collaborated with the product team to understand the onboarding and billing processes, including invoice creation, data entry, payment processing, and account management, which enhanced the overall efficiency of loan disbursement analysis. • Achieved a significant improvement in data entry processes by implementing a barcode system for new borrowers, streamlining the generation of customer details in the database. • Utilized PostgreSQL to spool weekly dataset ana
Education verified_user 0% verified
  • University of Exeter
    Master of Data Science with Artificial Intelligence
    University of Exeter
    Sep 2021 - Sep 2022 (1 year 1 month)
  • Babcock University
    Bachelor of Science (BSc) in Computer Science
    Babcock University
    Sep 2011 - Jun 2015 (3 years 10 months)
Projects (professional or personal) verified_user 0% verified
  • H
    Forecast Monthly and seasonal inventory dashboard
    Harris seeds
    Feb 2025 - Apr 2025 (3 months)
    The project focuses on forecasting monthly and seasonal inventory levels and sales at both the SRID and Price Class levels using the provided datasets from an Acumatica ERP system. Presenting findings in a Power BI dashboard that’s easy to use for sales reps and purchasing managers. One major challenge was data ambiguity, which could result in inconsistencies, such as having identical values for both forecasted and actual revenue. This required careful handling to ensure the integrity and reliability of the analysis. In the Data Modeling phase, I addressed issues related to duplicate Unique ID values before establishing the relationship cardinality between the Product and sales tables. Additionally, I utilized Metabase to create initial vis
  • AiCore
    PINTEREST DATA PIPELINE
    AiCore
    Jun 2024 - Sep 2024 (4 months)
    This project aims to replicate Pinterest's data processing system by creating an end-to-end pipeline on the AWS Cloud. The pipeline orchestrates data ingestion, storage, and real-time analysis, utilizing a range of technologies: - Amazon Elastic Compute Cloud (EC2) for computing resources - Amazon Simple Storage Service (S3) for scalable object storage - SQL for data querying and manipulation - Apache Kafka/AWS Managed Streaming for Apache Kafka (MSK) for streaming data processing - Amazon API Gateway for creating APIs - Spark for distributed data processing - Databricks for data engineering and analytics - Amazon Managed Workflows for Apache Airflow (MWAA) for workflow orchestration - Amazon Kinesis for real-time data streaming
  • AiCore
    Multinational Retail Data Centralization
    AiCore
    Jun 2024 - Dec 2024 (7 months)
    In my role focused on Multinational Retail Data Centralization, I was responsible for consolidating and managing sales data from various international sources to enhance reporting accuracy and efficiency. I utilized a Python script to transform and extract sales datasets from diverse formats, including S3 buckets, PDFs, and CSV files, into my local PostgreSQL database. Additionally, I performed SQL queries to address critical business questions, ensuring that the data analysis was aligned with strategic objectives. To facilitate better decision-making, I created a comprehensive dashboard using Power BI, showcasing key metrics that drove actionable insights. This integration of data analytics and cloud computing tools significantly improved