Eduardas Valaitis

About Eduardas Valaitis

At PwC, I lead a diverse team of data scientists, engineers and management consultants…

Experience

1. Director, Pharma and Life Sciences with Focus on Pharma R & d Analytics

   PwC

   Jun 2014 — Present

   Highlight / Protocol Optimization and Study Planning – Intelligent Clinical Trials I lead programs at top pharma companies to embed analytics, AI & automation into protocol design, study planning and execution processes to accelerate trial conduct, reduce costs, optimize the use of DCT capabilities and increase patient diversity. These programs typically require: ⁃ A multi-year business case for prioritized analytics & automation investments ⁃ A custom data platform (AWS or Azure) for aggregating & conforming protocol, operations, clinical and RWD data ⁃ NLP pipeline to extract unstructured protocol data and map them to standards (e.g, SNOMED) ⁃ Machine learning & nonlinear optimization methods to predict & optimize clinical trial outcomes (e.g, SSU, enrollment speed, grant payments) ⁃ Interactive UIs that enable users to optimize prospective clinical trial design and country & site allocation to achieve faster enrollment, reduce costs and increase patient diversity ⁃ OCM strategy & training to drive adoption across functions The Intelligent Clinical Trial solutions deployed at top pharma clients drive 20-30% cost savings and trial cycle time reductions. Other Engagements - Developed flexible SIRD COVID-19 models and automated capability to assess the impact of COVID-19 on clinical trial portfolio reflective of geo, protocol and patient population risks - Developed a platform for the aggregation, curation, analysis and visualization of 100s of historical immuno-oncology trials to drive the use of these data assets to optimize protocol design, identify cross-trial safety signals & respond to regulatory inquiries - Developed data & analytics strategy for risk-based site monitoring and implemented a monitoring application that predicts future site and portfolio quality risks - Utilized RWE data (claims, scripts & disease incidence) to conduct market segmentation analysis and model drug demand to inform pricing strategy for a new large molecule drug

Skills

• Finance
• Predictive Analytics
• Sas
• Economics
• Data Mining
• Business Analysis
• Management Consulting
• Analysis
• Strategy
• Analytics
• Statistics
• Due Diligence
• Sarbanes-Oxley Act
• Business Valuation
• Research
• Corporate Finance
• Data Analysis
• Quantitative Analytics
• Management
• Auditing
• Financial Modeling
• Strategic Planning
• Forecasting
• Business Process Improvement
• Market Analysis
• Statistical Modeling
• Market Research
• Vba
• Valuation
• Financial Analysis
• Internal Controls
• Econometrics