Data Scientist with experience in data pipelines, ETL, machine learning, BI dashboards, and analytics. Has worked on customer churn prediction, clustering, recommendation systems, and marketing analytics using Python, SQ…
Data Scientist with experience in data pipelines, ETL, machine learning, BI dashboards, and analytics. Has worked on customer churn prediction, clustering, recommendation systems, and marketing analytics using Python, SQL, Power BI, Azure, and related data tools.
Designed and built data pipelines on Azure Data Factory to move data from transactional systems into analytics databases.
Created ETL scripts in Python to transform raw data from MongoDB and stored it in PostgreSQL into formats usable for reporting and models.
Developed churn prediction models in Python achieving 15% lift in customer retention rates.
Increased CLV by over $0.5M for a client.
Developed and implemented machine learning models to analyze customer behavior and predict future trends, resulting in a 20% increase in revenue.
Built classification models (logistic regression, random forest) to predict customer churn.
Achieved 15% lift in retention.
Communicated complex technical concepts to non-technical stakeholders, resulting in improved understanding of data analysis results and increased engagement.
Created clustering models (K-means, hierarchical) to uncover distinct customer segments and buyer personas.
Used tools such as Python, notebooks, SQL, NumPy, scikit-learn, and pandas.
Built recommendation system model leading to a 10% increase in average order size.
Led data science team to deliver analytics and modeling projects solving key client business issues.
Communicated data stories to executive audiences via compelling Power BI dashboards and presentations.
Determined optimal data architecture and infrastructure to enable scalable analytics.
Collaborated closely with product managers and engineers to identify model integration requirements.
Managed end-to-end model development lifecycle including ideation, experimentation, validation, and monitoring.
Collaborated with data science and engineering teams to deliver end-to-end machine learning solutions for clients.
Created dashboards and reports using Excel and Power BI to communicate insights results to stakeholders.
Responsibilities
• Developed and implemented machine learning models to analyze customer behavior and predict future trends, resulting in a 20% increase in revenue.
• Worked with cross-functional teams to develop data pipelines and integrate cloud services, reducing data processing time by 50%.
• Conducted statistical analysis to identify key performance indicators and develop data-driven recommendations for improving business operations.
• Communicated complex technical concepts to non-technical stakeholders, resulting in improved understanding of data analysis results and increased engagement.
• Developed automation scripts using Python to streamline cloud operations and improve scalability.
• Used statistical and machine learning techniques to extract insights and build predictive models. This could include building models for classification, regression, or clustering, depending on the business problem at hand.
• Conducted data analysis to identify key performance indicators and develop data-driven recommendations for improving business operations.
• Communicated technical concepts to non-technical stakeholders, resulting in increased engagement and understanding of data analysis results.
• Developed predictive models for customer behavior and product demand, resulting in improved inventory management and increased revenue.
• Collaborate with platform teams and solution architects to evolve big data platforms and evaluate various data science technologies and services
• Collaborate with data science and engineering teams to deliver end-to-end machine learning solutions for our clients
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, feature engineering, and software architecture patterns
• SQL skills; Understanding of relational databases, business data, and the ability to write SQL queries against a variety of data sources
Built BI solutions utilizing Excel and Power BI to extract data insights and enable self-service analytics.
Ran A/B testing to improve conversion paths.
Decreased cost-per-lead by 20% in 2023.
Developed statistical models to solve problems like forecasting and predictive analytics using Python.
Quantified marketing impact on business KPIs like revenue, customer acquisition, and LTV.
Managed complex SQL datasets, ETL processes and analytics tooling to enable self-service reporting for the global marketing team.
Coded algorithms in Python that determine customer lifetime value cohorts to target for retargeting.
Migrated reporting from manual processes to automated Power BI dashboards providing self-service access to marketing KPIs.
Developed insightful visualizations and dashboards that led to optimization of content strategy.
Achieved 25% increase in web traffic.
Applied marketing segmentation to uncover nuances in customer behavior and inform personalization programs targeting high-value segments.
Led analytics projects from inception through delivery of insights for annual marketing budget.
Partnered with stakeholders to ensure actionability.
Prepared a Data Science curriculum to train 150+ students which was approved by partner organizations Microsoft 4Afrika and GIZ.
Led and trained a group of students in the Azubi Africa Data Science training and recorded a 97% pass.
Acted as a women lead in charge of advising on diversity and inclusion issues and creating programs to support the course.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
