Operations & Business Intelligence · Data Analyst · Berlin, Germany

Working in operations and BI teams in the oil and gas industry · MSc Data Analytics (in progress) · Expertise spanning supply chain, fuel distribution, demand forecasting, and operational performance

I'm not a career-changer, learning analytics from scratch. I'm an experienced operations and business intelligence professional who has spent 7 years making data-driven decisions in one of the world's most complex industries, and I'm now adding formal analytical and technical depth to match that operational credibility.

I worked with the kind of data most analysts only see in cleaned datasets: real-time operational data, fuel stock variance reports, supply chain performance metrics, and executive dashboards used to make million-dollar decisions. I understand what good analysis looks like from the perspective of someone who has had to act on it.

One question a business owner asked me recently stayed with me: "After all your analysis - what does poor service actually cost a business?" That question became a project. The answer was £6.05 million in customer lifetime value at active churn risk. That is the kind of analysis I build.

• 7 years of hands-on experience in operations and business intelligence; not theoretical, not academic
• Deep domain expertise in fuel distribution, supply chain management, and operational performance analysis
• Proven ability to translate complex operational data into insights that decision-makers can act on immediately
• Experience leading BI teams, understanding what analysts need to produce and what stakeholders need to receive
• A habit of asking not just "what does the data say?" but "what does this cost the business and what do we do about it?"
• Currently completing an MSc in Data Analytics to formalise technical skills in Python, statistical modelling, and machine learning

Python MySQL SQL Tableau Excel UCI Dataset Customer Analytics Business Intelligence CLV SLA Analysis

End-to-end BI project answering one question: what does poor service actually cost a business? Built across six Python notebooks, 16 SQL queries, a live Tableau dashboard, and a four-tab Excel executive summary, mirroring how a real BI team works from data engineering through to board-level reporting.

The project connects operational performance data to customer behaviour and business outcomes. Every finding follows a four-step framework: Analysis → Implication → Recommendation → Risk of Inaction.

Key findings:

• 89.4% SLA breach rate, nearly nine in ten tickets unresolved within target
• £6.05M in customer lifetime value at active churn risk (33.5% of total portfolio)
• CSAT of 2.49 vs German market average of 4.05, a retention emergency in the primary growth market
• LOC_03 and LOC_07 breaching at 94%+ both Germany-based, both destroying the expansion opportunity
• At 20% churn among at-risk customers: £1.21M in projected lifetime value loss

View live dashboard on Tableau Public

Excel Tableau Pivot Tables Sales Analysis Logistics Stock Variance Fuel Distribution KPI Design

Dual dashboard analysing fuel sales revenue, logistics operations, and stock variance across 5 regions and 15 stations built in both Excel and Tableau. Includes industry-specific stock variance analysis (overall 1.19% variance rate) with breakdown by fuel type reflecting real operational loss patterns: Petrol 1.63%, Diesel 0.94%, LPG 0.49%. The Tableau version adds a combo chart and interactive web publishing not possible in Excel.

View live on Tableau Public

Excel Pivot Tables Slicers Supply Chain Inventory Analysis Supplier Performance

Excel dashboard tracking product demand, inventory gaps, and supplier performance across 5 German warehouse locations. Identifies consistent overstocking across all 4 product categories, a 2.5× delivery time gap between best and worst supplier, and a revenue vs. volume mismatch pointing to a clear pricing opportunity.

MySQL SQL Window Functions Aggregation Supply Chain Supplier Performance Inventory Analysis

10-query MySQL analysis of the same supply chain dataset answering business questions across product performance, supplier reliability, inventory efficiency, and warehouse analytics. Deliberately built to complement and validate the Excel dashboard above, Query 10 reproduces the exact KPI summary (€1,349,500 revenue, 31.57% margin, 6.1 day avg delivery) confirming full analytical consistency across tools.

Demonstrates advanced SQL including RANK() OVER (PARTITION BY) window functions, multi-column GROUP BY, derived metrics (sell-through rate, fulfilment rate, revenue per cost euro), and COUNT(DISTINCT). Key finding: all four product categories show sell-through rates below 80%, pointing to business-wide over-ordering with EV Chargers the worst performer at 71.7%.

The following projects were completed as part of my Master's programme. They demonstrate applied Python skills, predictive modelling, time series analysis, and algorithmic problem-solving on real analytical problems.

Python pandas numpy scikit-learn Predictive Modelling EDA

Exploratory data analysis and predictive modelling to identify key variables associated with customer churn risk. Translates analytical findings into targeted retention interventions combining technical modelling with business relevance.

Python pandas numpy matplotlib Time Series Forecasting

Time series analysis on historical data to identify trends, seasonality, and demand patterns. Directly applicable to fuel procurement planning, operational scheduling, and inventory management, skills I understand from both the analytical and operational side.

Python Jupyter Notebook Algorithms Problem-solving

Structured problem-solving and algorithmic thinking applied to computational challenges. Demonstrates foundational programming logic and analytical reasoning in Python.

Before formalising my analytics skills, I spent 7 years in the oil and gas industry as a team lead in operations and business intelligence. I've built and presented dashboards to executive stakeholders, managed operational data across complex supply chains, and led teams responsible for turning raw operational data into business decisions.

That experience taught me things no course can fully replicate: how messy real operational data actually is, what questions decision-makers are actually trying to answer, and what it means for an analysis to be genuinely useful rather than just technically correct.

My MSc in Data Analytics is giving me the formal technical foundation: Python, machine learning, statistical modelling to work at the full depth of modern data analytics. I'm building a portfolio that reflects both the domain expertise from 7 years in industry and the technical rigour from formal training.

I'm based in Berlin and open to data analyst, business intelligence, and operational analytics roles across industries with particular depth in energy, supply chain, logistics, customer operations, and operations-intensive sectors.

• LinkedIn: linkedin.com/in/hettymens
• GitHub: github.com/Esitty
• Tableau Public: public.tableau.com/app/profile/henrietta.mensah