Alar Saad

Designed and deployed an internal AI workflow to analyze service notes and medical documents, producing structured outputs for Underwriting, Claims, and Actuarial teams. Built end-to-end pipelines (Python, SQL, Azure OCR/Document extraction, OpenAI/Azure OpenAI) that turn free-text logs + documents into validated JSON results. Translated business requirements into scalable technical solutions by aligning with domain experts and operational stakeholders to define decision logic and success criteria. Implemented production reliability: checkpoint recovery for long runs, retries with rate-limit handling, and fallback logic to prevent restart-from-zero failures. Defined prompt standards and validation layers (strict schemas, evidence rules, normalization, exception handling) to keep outputs consistent and reduce manual review. Integrated multiple data sources (SQL datasets + document/OCR outputs) into a Streamlit app used by stakeholders for guided case handling. Developed a GPT-based assistant for multi-step decision processes, combining policy rules + document analysis, in close work with domain experts. Measured quality with run diagnostics and operational metrics (consistency, exception rates, latency) and improved results through controlled prompt/version changes.

Extracted, organized, and maintained data infrastructure and datasets to support actuarial and business analytics. Performed ongoing data quality checks and validations, using statistical reasoning to identify anomalies and ensure reliable reporting. Conducted profitability and performance analyses across portfolio segments, supporting commercial and operational decision-making. Delivered actuarial analyses across multiple domains, including market share and trend analysis. Developed and optimized end-to-end data retrieval, processing, and analysis procedures, leveraging automation and AI/ML approaches where relevant. Diagnosed business challenges through structured analysis and translated findings into actionable recommendations. Collaborated with and led cross-functional data initiatives, working closely with stakeholders across the organization. Built and implemented AI/ML-driven workflows to improve decision-making processes and operational efficiency.

Overseeing a wide range of operations pertaining maritime shipping freights including opening shipping orders opposite the clients and shipping companies. Meticulously monitoring shipping timetables, handling export customs processes, and tracking shipments opposite customs agents abroad until actual delivery to the clients and issuing of invoices. Successfully handling complex export processes opposite customs agents, efficiently streamlining shipping operations, exhibiting outstanding punctuality and organizational proficiency.

Handling a wide range of customer service operations, identifying customer needs and providing real-time solutions, promoting and marketing products to prospective customers as well as customer service initiatives, achieving all customer service goals.

- Practicum is a 7-month intensive online program designed to train talents to be successful Data Analysts. - Coursework Includes: Python (Pandas and NumPy), Excel, Relational Databases (PostgreSQL, MySql), EDA (Exploratory Data Analysis), Data Visualization (Maplotlib, Seaborn, Plotly), Dashboards (Dash, Tableau), Automation (basic Bash, AWS), GitHub, A/B Testing, Machine Learning (Forecasting and Segmentation), KPIs, **12 real data-based projects.**