Edward Eni

– Designed and implemented ETL pipelines using Apache NiFi, improving data processing efficiency. – Built predictive models to optimize financial data analysis, enhancing decision-making. – Conducted statistical analysis on financial trends, leading to actionable insights for policy recommendations. – Automated data reporting with Python & SQL, reducing manual work by 4 hours per week. – Developed interactive dashboards (Power BI, Tableau) for real-time financial monitoring. – Support the Data Governance Office to coordinate data governance activities centrally. – Identify Stakeholders to data related tasks. – Act as point of contact to Data Governance related tasks and communicate directly with Data Governance Lead, technical delivery teams, suppliers and other stakeholders involved. – Support Cataloguing the existing coverage of Data Governance artefacts, identify and document potential gaps and support the DGL and DGO related groups in determining the priority for addressing issues. – Support DGO with best-practice creation, agreement and use of Data Governance artefacts, such as business dictionaries/glossaries, policies and quality standards.

– Managed and processed large volumes of operational data with a high degree of accuracy and integrity, ensuring the reliability of downstream reporting systems. – Utilized Advanced Microsoft Excel functionalities (Pivot Tables, VLOOKUP, conditional formatting) to perform preliminary Exploratory Data Analysis (EDA) and data cleaning, identifying anomalies and trends to improve data quality prior to final submission. – Built and strengthened standardized, centralized data governance controls across cloud/applications/systems; defined enterprise-wide operational standards aligned with data strategy, data quality, and architectural principles; led customer/user engagement to understand business data requirements and use cases; partnered with cross-functional teams to measure and record data quality, compliance, cost, and efficiency benefits; identified Data Owners, defined roles/responsibilities, supported Data Governance Advisers, projects, programmes, and initiatives, and championed the procurement and implementation of Data Governance tools including building the Business Glossary and defining Critical Data Elements. – Created awareness on AI/LLM usage at TPR and developed communication packages for Critical Data Elements; supported the Business Continuity team on system criticality, built strong IT relationships to break siloed working, and ensured consistent collaboration across teams to improve data governance maturity and enterprise-wide data management practices.

– Drove a 25% increase in content strategy efficiency by designing and implementing a time-series social media traffic forecasting model (using Facebook Prophet) that provided predictive insights for marketing resource allocation. – Engineered and deployed a personalized, machine learning-based recommendation system, which directly contributed to a 20% surge in overall platform user engagement and improved content discoverability. – Conducted deep-dive user behavior analysis and advanced statistical modeling to define and track key growth metrics, delivering actionable, data-driven recommendations that resulted in a 20% improvement in user retention strategies. – Presented analytical findings and model performance metrics to senior management, effectively translating complex models into clear strategic implications for digital marketing campaigns.

– Led technical deep dives and code reviews for the ML team, establishing best practices for model robustness, documentation, and maintainability across all deployed production systems. – Mentored three junior engineers on MLOps principles, Python best practices, and the full model lifecycle, significantly accelerating their on-boarding and contribution to core projects. – Engineered and optimized automated ML pipelines for fault detection and 3D segmentation, improving segmentation accuracy by over 15% and ensuring real-time data processing capabilities across large-scale geophysical datasets. – Spearheaded the design and deployment of Deep Learning algorithms for 3D object detection and localization, which improved threat identification accuracy in critical infrastructure data. – Managed cross-functional collaboration between data science and operations teams to deploy a user-friendly desktop application (EXE), ensuring seamless integration of vision models into existing workflows and securing buy-in from key technical stakeholders. – Drove engineering maturity by standardizing model validation and ground truth reconstruction processes.

– Utilised advanced Excel skills and advanced VBA coding for the development of macros to automate processes across multiple departments to increase efficiency and accuracy. – Skilled in data cleaning, mining and analysis (SQL, SSRS, Excel, Advanced VBA, R, Python), business intelligence, statistical analysis, predictive modeling, online marketing analytics and data visualisation. – Specialised in digital marketing analysis including email marketing, SEM, Facebook marketing, contextual marketing, retention and attrition modeling as well as segmentation and regression analysis. – Worked closely with marketing specialists and senior management to guide strategy through advanced data analysis of large datasets, integrating and combining data from multiple sources (internal data via SSRS, Google analytics + google adwords or SQL databases) to provide actionable insights to optimise marketing ROI. – Present monthly analysis of KPIs to C-level senior management and stakeholders. Manage dashboards and reports to translate large volumes of quantitative data into clear and profitable insights. Worked close with database administrators to translate business requirements into specifications to develop MS SQL Reports.

– Gathered and analyzed financial transaction data to provide ad-hoc business valuations for multinational clients (ranging from $35m to $15b in total assets) in the SAAS, aerospace and defense, cannabis, retail and consumer products and private equity industries for goodwill impairment, purchase price allocation, business enterprise valuation, stock-based compensation and tax reporting purposes – Project experience: – Identified business operation bottlenecks and generated store-level insights for a Fortune 500 client by delivering automated dashboards in Power BI that tracked KPIs, including revenue growth, contribution margins, occupancy costs, etc. for all of its 400+ stores worldwide – Informed industry benchmarking reports and valued client companies relative to industry peers using Guideline Public Company and Guideline Transaction approaches by extracting and analyzing financial market data employing Capital IQ, Thomson Reuters and Oxford Economics databases