Tasks

• Provide expert services in pharmaceutical R&D for developing deep learning models for drug discovery, including predictions of organic compound solubility and interactions with proteins and DNA.

Challenges

Solutions

• Specialized service combining ML expertise with chemistry and drug discovery.
• Development and deployment of deep learning models to predict organic compound solubility and interactions with proteins/DNA.
• Utilization of extensive datasets and advanced algorithms for accurate insights.
• Seamless integration with existing R&D workflows to ensure minimal disruption.
• Continuous support and model updates for adapting to new data and research needs.

Outcomes

• Achieve a 15-20% reduction in research time and costs, accelerating the drug discovery process and improving the success rate of potential drug candidates.

Technologies Used

Tasks

• Develop an AI-driven ecosystem to enhance HPLC capabilities in pharmaceutical R&D, enabling peak prediction, automated integration, and compliance with regulatory standards and GMP.

Challenges

Solutions

• Develop an AI-powered ecosystem to enhance HPLC capabilities.
• Predict peak retention times and integrate peaks automatically per GMP and regulatory standards.
• Utilize ML models trained on historical chromatographic data for real-time predictions and integrations.
• Include a user-friendly interface for parameter monitoring and compliance.
• Seamlessly integrate with existing LIMS for streamlined data management and record-keeping.

Outcomes

• Achieve a 20-25% increase in operational efficiency, reducing manual workload and enhancing the accuracy of chromatographic analyses.

Technologies Used

Tasks

• Implement AI/ML solutions to forecast material consumption and sales, optimizing procurement, supply chain, and production quantities in the Pharma industry.

Challenges

Solutions

• Implement ARIMA and LSTM models for time series forecasting of material usage and sales.
• Integrate models with ERP and CRM systems for seamless data flow.
• Establish a robust data governance framework.
• Enable continuous model retraining to adapt to market changes.
• Enhance procurement planning and production schedules to reduce material waste.

Outcomes

• Achieve a 10-15% reduction in material waste, improving overall efficiency and reducing costs.

Technologies Used

Tasks

• Audit current IT architecture.
• Analyze reporting processes across departments.
• Recommend solutions for analytic function optimization.
• Gather requirements for reporting and analytics.
• Define master data systems for each category.
• Propose new IT architecture for system integration.
• Outline development plan for the new system.

Challenges

The main challenge was to develop a comprehensive strategy to automate data collection and reporting processes, integrate various data systems, and improve data-driven decision-making capabilities for Servier Ukraine.

Solutions

• Established unified reporting standards for all data categories.
• Created a single Master Data system per category.
• Enabled automated data exchange across systems to reduce manual tasks.
• Built an automated reporting system for recurring reports.
• Recommended MS Power BI as the primary reporting tool.

Outcomes

• Automated data collection and reporting.
• Enhanced data integration and minimized silos.
• Strengthened decision-making with predictive analytics.
• Reduced manual tasks, boosting data efficiency.
• Improved user experience with a modern interface.

Technologies Used

Tasks

• Developed visualizations for client segments and profiles
• Predicted churn likelihood with machine learning models
• Set up data warehouse and database schema
• Automated data updates for Power BI dashboard
• Implemented alert system for data updates and exchanges
• Created tools for churn retention strategies
• Compiled detailed project documentation

Challenges

Building a robust data infrastructure and predictive models for effective churn retention.

Solutions

• Set up a data warehouse (likely AWS Redshift or Google BigQuery).
• Use scikit-learn models and gradient boosting for churn prediction.
• Automate data processes and implement retention strategies.

Outcomes

Improved client retention and data-driven decision-making through advanced analytics.

Technologies Used

Tasks

• Develop a complex solution for taxi aggregator service
• Analyze user feedback in various languages
• Implement a multilingual pipeline
• Establish quality control measures
• Create custom-tailored categories

Challenges

Efficiently analyzing users’ feedback in various languages. The need for a multilingual pipeline, quality control measures, and the requirement for custom-tailored categories.

Solutions

• Developed a complex solution for analyzing user feedback
• Created a multilingual pipeline for processing reviews
• Implemented quality control measures
• Established custom-tailored categories for feedback analysis

Outcomes

• Our comprehensive consulting solution resulted in a 20% decrease in the amount of negative feedback (1-2 stars)

Technologies Used

Tasks

• Enhancing AI models for improved efficiency.
• Transitioning systems to AWS for scalability.
• Consolidating data from various sources.
• Aligning releases with client design and usability needs

Challenges

The main challenge was migrating to AWS managed services to enhance performance and scalability, consolidating data sources, and integrating client-specific design and usability needs.

Solutions

• Migrated Adthena’s system to AWS, boosting scalability and performance.
• Achieved 20%+ cost savings through AI optimizations.
• Created complex reporting by aggregating data from Google, third parties, and Google Ads.
• Focused on user-centric design to exceed client expectations.
• Used Agile/Scrum for seamless collaboration and smooth execution.

Outcomes

• AWS migration improved scalability and performance, handling higher loads efficiently.
• AI optimizations reduced costs by over 20%.
• Design updates aligned releases with user needs, enhancing experience.
• Data consolidation and system upgrades boosted service efficiency for Adthena.

Technologies Used

Tasks

• Build a single Master Data system
• Data collection and aggregation
• Data enrichment and processing
• Develop a business intelligence system

Challenges

Working with a database containing over 150 GB of data and processing more than 10 million transactions.

Solutions

• Built a single Master Data system
• Implemented key functions including data collection and aggregation, as well as data enrichment and processing
• Developed a business intelligence system for data-driven decision-making

Outcomes

• Built a single Master Data system
• Key functions included data collection and aggregation, as well as data enrichment and processing
• Developed a business intelligence system for data-driven decision-making

Technologies Used

Tasks

• Develop a predictive model on the wind energy production forecast
• Analyze historical data for accuracy
• Implement and test the model

Challenges

Accurately forecast wind energy production based on historical data.

Solutions

• Developed a predictive model for wind energy production
• Utilized historical data for training and testing the model
• Implemented machine learning techniques to enhance forecast accuracy

Outcomes

• Model Forecast – mean Absolute Percentage Error was <10

Technologies Used

Tasks

• Analyze customer behavior
• Identify upselling and cross-selling opportunities
• Leverage data analytics to boost revenues
• Increase the average basket size
• Understand key customer segments
• Retain customers in a competitive market

Challenges

Solutions

• Developed a new recommender engine to grow the average basket size
• Implemented a new churn retention strategy based on model’s predictions
• Leveraged data analytics to understand key customer segments and opportunities

Outcomes

• The new recommender engine allowed to grow the average basket size by 0.4 items
• The new churn retention strategy based on our model’s predictions allowed early detection of churning clients, which led to a decrease in churn from almost 20% to 15%

Technologies Used

Tasks

• Establish a single client-consultant touchpoint for client questionnaires.
• Enable advanced analytics for consultants.
• Generate HRPP, Salary reports, and Graded Pay Structure.

Challenges

Solutions

• Developed a secure, single-touchpoint service with dynamic dashboards and drill-down capabilities.
• Built an autonomous system with a modern, user-friendly interface.
• Used historical data for predictive analytics and personalized recommendations.

Outcomes

Improved customer satisfaction with a user-friendly service, enhanced consultants’ effectiveness, enabling greater project scalability, reduced consultants’ data tasks, freeing resources, and showcased EY’s commitment to modern solutions and brand value.

Technologies Used