Morris Munene

I am a Data Scientist with 4+ years of experience building end-to-end machine learning and analytics solutions that drive measurable business growth. I've got proven track record in customer segmentation, churn prediction, causal inference and A/B testing across B2B SaaS and consulting environments. I'm skilled at translating complex analytical findings into executive-ready recommendations and embedding AI capabilities into self-service tools.

I'm adept at partnering cross-functionally with Product, Engineering, Marketing and Finance to align data strategy with commercial priorities.

Data Scientist at DreamTuners Global (2024-06 – Present)

• Analyzed subscriber demand patterns and behavioural cohorts across markets using Python and SQL, identifying high-value customer segments and informing targeting strategy increasing after-sales engagement by 22%.
• Designed and evaluated A/B tests on promotional campaigns, quantifying uplift and enabling data-driven allocation of marketing budgets across customer lifecycle stages.
• Built predictive segmentation models to score customers by retention risk and lifetime value, enabling proactive outreach that reduced churn exposure in key segments.
• Constructed interactive Power BI dashboards tracking KPIs across sales, service performance and parts supply accelerating evidence-based commercial decision-making for executive stakeholders.
• Architected Azure/Spark-based ETL pipelines and governed semantic data models to ensure consistent, trusted reporting across business units.
• Documented KPI definitions, model assumptions and reporting logic to standardize decision-making criteria and enable self-service analytics across the organization.

Data Scientist at DataForge Partners (2023-01 – 2024-05)

• Developed and validated causal and predictive models on heterogeneous datasets, improving demand forecast accuracy by 28% through rigorous feature engineering, error analysis and model selection.
• Applied cohort analysis and behavioural segmentation to identify demand trends and customer lifecycle patterns, informing strategic forecasting and planning decisions.
• Assessed model uncertainty and data quality limitations to support decisions under imperfect information communicating risk and assumptions clearly to non-technical stakeholders.
• Built incremental SQL/Spark ETL pipelines that reduced manual reporting time by 45%, enabling more responsive monitoring of business trends.
• Partnered with Product and Engineering teams to define analytical requirements, document data flows and align on measurement frameworks for key business outcomes.

Junior Data Analyst at Tribase Labs (2021-09 – 2022-08)

• Built subscriber churn prediction and behavioural segmentation models to prioritize high-value customer groups, improving retention targeting efficiency by 20%.
• Designed Power BI dashboards to visualize lifecycle KPIs and operational performance metrics for stakeholder decision support.
• Developed risk and segmentation models supporting compliance monitoring, fraud detection, and subscriber retention strategy.
• Translated technical model outputs into clear business recommendations bridging the gap between analytics and commercial action.

Data Analyst / Quantity Surveyor at Powerful Construction Limited (2022-09 – 2022-12)

• Analyzed cost and procurement data to optimize material purchasing decisions, reducing project costs by 15%.
• Built Excel and Power BI reports to track project performance, spending trends, and delivery risk for senior management.

B.Sc. in Computer Science – University of the People

Diploma in Quantity Surveying – Kiambu National Polytechnic