Senior Principal Data & Analytics Consultant

• Data Strategy & Advisory: Define data roadmaps and align analytics initiatives with business objectives to maximize impact.
• Full-Stack Analytics Implementation: Build scalable data pipelines and machine learning models using Python, R, SQL, and cloud platforms (AWS, GCP, Azure).
• Cross-Functional Leadership: Serve as the key liaison between technical teams and business stakeholders, ensuring project success and a data-driven culture.
• Business Intelligence & Visualization: Design intuitive dashboards and reports in Tableau, Power BI, and Looker to deliver actionable insights across the organization.
• Predictive Modeling & AI: Develop and deploy models for forecasting, customer segmentation, and recommendation systems that drive measurable business results.
• Data Literacy & Enablement: Coach technical and non-technical teams to translate data into strategic decisions and long-term value.

I built an entire business intelligence system from scratch. This allowed me to provide information from medical and business data to internal stakeholders as well as external pharmaceutical clients. I also co-authored scientific papers with the medical team, one of which was published in the SAGE Journal of Women's Health.

• Establishing and implementing company-wide data reporting standards for B2C.
• Approving and building the correct KPIs for everyone from C-Level to product owners and marketing managers.
• Coordinating with app developers, product teams, and infrastructure teams to ensure standardized and comparable data structure among different products.
• Using Python, R, and pSQL to develop a full pipeline that processes raw data from NoSQL database and converts it into KPI's and machine learning models, where results are displayed on Tableau.
• Automating the processes with Docker, as well as AWS EC2 Linux image when required.
• Preparing and holding two major data literacy and Tableau training sessions per quarter.

I built an entire business intelligence system from scratch. This allowed me to provide information from medical and business data to internal stakeholders as well as external pharmaceutical clients. I also co-authored scientific papers with the medical team, one of which was published in the SAGE Journal of Women's Health.

• Establishing and implementing company-wide data reporting standards for B2C.
• Approving and building the correct KPIs for everyone from C-Level to product owners and marketing managers.
• Coordinating with app developers, product teams, and infrastructure teams to ensure standardized and comparable data structure among different products.
• Using Python, R, and pSQL to develop a full pipeline that processes raw data from NoSQL database and converts it into KPI's and machine learning models, where results are displayed on Tableau.
• Automating the processes with Docker, as well as AWS EC2 Linux image when required.
• Preparing and holding two major data literacy and Tableau training sessions per quarter.

I worked fully with Microsoft as an external data scientist, where I supervised and developed statistical prediction models for marketing and sales managers, including but not limited to:

• Customer Churn Prediction: Building a model that assesses each customer individually and predicts whether a customer will be lost within the next period.
• Targeted Marketing Campaigns: Detecting which customers are most fit to be targeted with a specific product in future campaigns.
• Time Series Analysis: Helping managers detect anomalies and understand trends and seasonality in the sales data.

• Used Browserstack to automate online platform testing and prepared requirement documents.