Proactive Finance Systems Junior Analyst with a strong background in data coordination and predictive modelling, leveraging 18 months of financial analysis experience. Proficient in SQL and Python, having engineered auto…
Proactive Finance Systems Junior Analyst with a strong background in data coordination and predictive modelling, leveraging 18 months of financial analysis experience. Proficient in SQL and Python, having engineered automated ETL systems that enhance data reporting for C-suite decision-making. Achieved a 50% increase in website conversion rates through optimised A/B testing strategies. Equipped with the skills to elevate financial systems and processes, ready to contribute to a dynamic team and drive impactful outcomes.
Conducted financial predictive modelling on 18 months of historical sales data to forecast revenue under new pricing structures, validating a strategic pivot that increased recurring revenue and mitigated churn., Engineered automated ETL systems using SQL and Python to consolidate marketing and financial data, feeding real-time Looker Studio dashboards used for C-suite reporting., Built automated customer success tracking systems by integrating Intercom and Looker, allowing real-time monitoring of NPS, CSAT, and Employee KPIs., Optimised marketing ROI by running aggressive A/B tests on landing pages and simplifying the funnel, increasing website conversion rates from 2% to 3.
Contributed to channel growth from 400,000 to 1,000,000+ subscribers by conducting competitor analysis to identify content gaps in topics and framing devices., Developed data-backed ideation strategies using YouTube Analytics to pivot content toward high-demand topics, aligning video framing with audience retention trends., Maximised Click-Through Rate (CTR) by designing high-performing video packaging (titles/thumbnails) based on audience engagement data.
Problem: The business relied on manual reporting for revenue and marketing data, slowing down strategic decision-making.Tools: Built ETL pipelines using SQL and Python to feed live data into interactive Looker Studio dashboards.Outcome: Democratised real-time access to critical financial, marketing, and customer health (NPS/CSAT) data across the organisation. This transition from manual processes to automated reporting reduced weekly overhead by 12 hours. Furthermore, the resulting data agility empowered rapid A/B testing cycles , which directly increased website conversion rates from 2% to 3%.
Problem: Needed to identify the highest-value customer segment to optimise marketing efforts and scale the customer base.Tools: Used SQL to extract customer profiles and Python (K-Means Clustering & Regression) to correlate demographics with Lifetime Value (LTV).Outcome: Identified that professionals in Tech/Med fields had the highest LTV; the company adopted these findings to redefine its target audience strategy, leading to higher CSAT and NPS scores.
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