Contact Information:
Address: 42 Analytics Avenue, Rosebank, Johannesburg, 2196
Phone: 083 0000 000
Email: nolwazi.dlamini@email.com
LinkedIn: linkedin.com/in/spaninolwazidlamini
GitHub: github.com/spaninolwazidlamini
PROFESSIONAL SUMMARY
Innovative Data Scientist with 6+ years of experience in statistical analysis, machine learning, and predictive modeling. Proven expertise in extracting valuable insights from complex datasets to drive business decisions. Strong background in developing AI solutions for financial services and retail sectors. Adept at communicating technical concepts to non-technical stakeholders and translating business requirements into data-driven solutions.
TECHNICAL SKILLS
Programming Languages:
- Python, R, SQL, Scala, MATLAB
Data Science & Machine Learning:
- Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost
- Natural Language Processing, Computer Vision, Time Series Analysis
- Statistical Analysis, A/B Testing, Hypothesis Testing
Big Data Technologies:
- Hadoop, Spark, Kafka, Hive, Airflow
- AWS (S3, EMR, SageMaker), Azure ML
Data Visualization:
- Tableau, Power BI, Matplotlib, Seaborn, D3.js
PROFESSIONAL EXPERIENCE
Senior Data Scientist | Standard Bank, Johannesburg
May 2021 – Present
- Lead a team of 4 data scientists developing machine learning models for credit risk assessment, improving approval accuracy by 28%
- Designed and implemented a customer segmentation model using clustering algorithms that increased targeted marketing campaign effectiveness by 35%
- Created NLP models to analyze customer feedback from multiple channels, identifying key improvement areas that increased customer satisfaction scores by 15%
- Developed a real-time fraud detection system using anomaly detection algorithms, reducing fraudulent transactions by 42%
- Collaborate with business stakeholders to translate business problems into analytical frameworks
- Present findings and recommendations to executive leadership, influencing strategic decisions
- Mentor junior data scientists and provide technical guidance on complex projects
Data Scientist | Woolworths South Africa, Cape Town
March 2018 – April 2021
- Built predictive models for inventory management, reducing stockouts by 22% and overstock by 18%
- Developed customer lifetime value models that increased retention of high-value customers by 25%
- Implemented recommendation engines for e-commerce platform resulting in 15% increase in average basket size
- Created interactive dashboards for sales and marketing teams using Tableau
- Conducted A/B tests to optimize website user experience and conversion rates
- Collaborated with IT department to implement models into production systems
- Presented monthly insights to management on customer behavior patterns and trends
Junior Data Analyst | RMB Analytics, Johannesburg
January 2016 – February 2018
- Performed data cleaning, transformation, and exploratory data analysis on financial datasets
- Assisted in developing statistical models for market trend analysis
- Created automated reporting pipelines, reducing manual reporting time by 70%
- Conducted competitive analysis and market research using public datasets
- Maintained and updated databases of financial indicators and market performance metrics
- Supported senior analysts in developing presentations for client meetings
- Collaborated with cross-functional teams to ensure data quality and consistency
EDUCATION
MSc in Data Science
University of Cape Town 2014 – 2015
BSc Honours in Statistics
University of the Witwatersrand 2010 – 2013
Professional Certifications
- AWS Certified Machine Learning Specialist (2022)
- Microsoft Certified: Azure Data Scientist Associate (2021)
- TensorFlow Developer Certificate (2020)
- Tableau Desktop Certified Professional (2019)
PROJECTS
Predictive Maintenance System for Manufacturing Equipment
- Developed time series models to predict equipment failures before they occur
- Implemented IoT data processing pipeline using Kafka and Spark Streaming
- Reduced unplanned downtime by 45% and maintenance costs by 30%
- Technologies: Python, TensorFlow, Kafka, Spark, AWS IoT
Sentiment Analysis for South African Financial Markets
- Created NLP models to analyze social media and news sentiment regarding JSE-listed companies
- Built trading signals based on sentiment indicators, achieving 18% ROI in backtesting
- Technologies: Python, NLTK, BERT, Vader, AWS Comprehend
Customer Churn Prediction Model
- Developed machine learning models to identify customers at risk of churning
- Implemented feature importance analysis to identify key churn drivers
- Achieved 87% accuracy in predicting customer churn
- Technologies: Python, scikit-learn, XGBoost, SHAP
PUBLICATIONS & PRESENTATIONS
- “Machine Learning Applications in South African Banking,” FinTech Africa Conference, 2022
- “Predicting Consumer Behavior Using Ensemble Methods,” Journal of Data Science, 2021
- “Explainable AI for Financial Services,” AI Summit South Africa, 2020
ACHIEVEMENTS & ADDITIONAL INFORMATION
- Award: Best Data Science Project, Standard Bank Innovation Challenge, 2022
- Hackathon Winner: First place at DataHack 2021, developing solutions for healthcare optimization
- Community Leadership: Founder of Women in Data Science Johannesburg chapter
- Languages: English (Fluent), Zulu (Native), Sotho (Conversational), French (Basic)
References available upon request