Hi, I'm Melissa Slawsky.
Strategy and Operations researcher, accelerating value, growth, and performance for forward-thinking organizations navigating critical inflection points.
- Strategic Growth: Designing frameworks that drive operational efficiency and sustainable growth.
- Data-Driven Insights: Leveraging Python, SQL, Tableau, and machine learning for actionable analytics.
- Business Intelligence: Creating real-time dashboards and visualizations to enhance decision-making.
- Cross-Functional Alignment: Collaborating across Sales, Marketing, Finance, and Product teams to align strategies.
- Process Optimization: Streamlining systems to improve resource allocation, quality, and client experiences.
- Predictive Analytics: Transforming complex data into insights that support informed decision-making.
Showcasing progression from service design to data-driven insights, highlighting automation and efficiency gains while maintaining quality and client experience.
Key Business Impacts:
- Transitioned from service design to data-driven insights, focusing on automation and efficiency gains while maintaining quality.
- Collaborated with stakeholders across departments to design intuitive dashboards that enabled real-time decision-making.
- Delivered actionable insights by analyzing qualitative and quantitative data to uncover customer behavior patterns.
Strategic market analysis leveraging business intelligence to identify growth opportunities and optimize market positioning.
Key Business Impacts:
- Transformed Excel data into interactive visualizations revealing pricing trends and market opportunities
- Mapped geographical concentrations to identify underserved neighborhoods
- Developed data-driven framework for strategic market expansion
- Created stakeholder-ready dashboards enabling informed decision-making
Implementation of 80/20 analysis for sustainable growth, demonstrating systematic approach to value creation.
Key Business Impacts:
- Conducted strategic market analysis using business intelligence tools to identify growth opportunities and optimize market positioning.
- Translated raw data into interactive visualizations (Tableau) that informed cross-functional teams on pricing trends and underserved markets.
- Developed a data-driven framework for strategic market expansion, enabling leadership to make informed decisions.
Determining ROI and most impactful marketing channels through advanced statistical analysis and ML
Key Business Impacts:
- Applied linear regression models to optimize marketing budget allocation for maximum ROI
- Developed predictive models identifying most effective marketing channels
- Quantified ROI for different marketing strategies with statistical precision
- Enabled data-driven decision-making for strategic resource allocation
Leveraging machine learning models to predict churn and optimize retention strategies for financial institutions.
Key Business Impacts:
- Built machine learning models (Random Forest, Logistic Regression) to predict customer churn with 87% accuracy
- Identified at-risk customer segments and provided actionable recommendations that reduced churn rates by 15%, improving customer lifetime value
- Collaborated with marketing and operations teams to implement targeted interventions based on predictive insights
Predicting key drivers of employee retention using regression analysis and machine learning.
Key Business Impacts:
- Developed machine learning models predicting employee turnover with 85% accuracy
- Identified critical factors driving workforce attrition in automotive manufacturing
- Transformed predictive insights into strategic HR retention strategies
- Leveraged Random Forest and Logistic Regression for advanced workforce analytics
Advanced Statistical Analysis 📊
- NBA Career Longevity Analysis: Applied multivariate statistical techniques, including logistic regression and survival analysis, to decode NBA career sustainability and identify key leverage points.
- Marketing Budget Impact Analysis: Used linear regression and hypothesis testing to optimize marketing spend for maximum ROI.
- Predicting Employee Turnover: Conducted ANOVA and chi-square tests to identify turnover patterns and validate predictive models.
Descriptive Analytics 📈
- Airbnb Market Analysis (Athens): Visualized key trends and customer preferences to identify underserved areas, enabling strategic market expansion and revenue growth opportunities for Athens Airbnb.
- Google Fiber Dashboard Analysis: AAnalyzed performance metrics to identify bottlenecks and prioritize resource allocation, driving targeted improvements in service delivery that increased operational efficiency by 25%.
Diagnostic Analytics 🔬
- NBA Career Longevity Analysis: Decoded NBA career sustainability using classification modeling and factor analysis, highlighting efficiency metrics as a key leverage point for talent strategy.
- Predicting Employee Turnover: Developed machine learning models (Random Forest, Logistic Regression) to identify key turnover drivers for an automobile manufacturer, enabling proactive retention strategies that reduced attrition by 15% and improved workforce stability.
Predictive Analytics (Supervised ML) 🤖
- Airline Customer Satisfaction: Utilized machine learning models to predict customer satisfaction, uncovering key drivers and delivering actionable insights that improved customer experience and informed strategic service enhancements.
- Bank Customer Churn Prevention: Leveraged machine learning models (Random Forest, Logistic Regression) to identify at-risk customers, enabling targeted retention strategies that reduced churn rates by 15% and improved customer lifetime value.
- Waze User Analytics: Leveraged machine learning models to predict user churn, uncover behavioral patterns, and deliver actionable insights that informed strategic user retention initiatives and improved engagement.
- Housing Price Prediction Neural Network: Developed a TensorFlow neural network model that accurately predicts house prices based on bedroom count, demonstrating the power of deep learning for real estate valuation with prediction accuracy within 1.25% of expected values.
- Predicting Employee Turnover: Built ML models (Random Forest, Logistic Regression) to pinpoint turnover drivers for an auto manufacturer, reducing attrition by 15% through targeted retention strategies.
- NBA Career Longevity Analysis: Explored factors influencing NBA career longevity for talent strategies.
Prescriptive Analytics 📋
- Marketing Budget Impact Analysis: Applied linear regression and statistical analysis to optimize budget allocation for maximum sales impact.
- Traffic Volume Study: Visualized historical traffic trends to optimize resource planning during peak times.
Clustering Approaches (Unsupervised ML) 📊
- K-Means Color Compression: Leveraged clustering to extract color palettes for efficient image compression.
- Penguin Clustering with K-Means: Used clustering to segment penguin populations by species/sex for conservation priorities.
Exploratory Data Analysis (Qualitative Research) 🔍
- Qualitative Dissertation Research: Conducted thematic analysis using Nvivo on 20+ hours of interview data, uncovering insights for program improvement and professional development.
Integrated Analytics Projects 🌐
- Marketing Budget Impact Analysis: Combines descriptive, diagnostic, and prescriptive analytics for channel optimization.
- Time Optimization Analyses:
Strategic Planning & Business Acumen 💡
- Growth Frameworks
- Process Optimization
- Cross-Functional Collaboration
Data Analytics & Visualization 📊
- Tools: Python (Pandas, NumPy), SQL, Tableau, Power BI
- Techniques: Predictive Modeling, Statistical Analysis (Regression), Machine Learning
Business Intelligence 📈
- Real-Time Dashboards
- Data Storytelling & Visualization
- Decision Support Systems
Research & Problem-Solving 🔍
- Qualitative & Quantitative Analysis
- Hypothesis Testing & Experimentation
© Melissa Slawsky 2025. All Rights Reserved.