Saddik M.D.

About Saddik M.D.

I\'m Saddik Mohammad, a Senior Data Scientist with 9+ years of experience using machine learning, data engineering, and cloud technologies to solve real-world business problems. I’ve worked across healthcare, finance, logistics, and telecom helping teams turn complex data into clear, useful solutions.I build predictive models, design automated data pipelines, and create tools that help companies move faster and smarter. I focus on making data work whether it’s reducing risk, cutting costs, or improving decisions.Skills:Python | SQL | Machine Learning | NLP | PySpark | Apache Spark | Tableau | Power BI | AWS | Azure | GCP | TensorFlow | Scikit-learn | Airflow | AWS Glue | Snowflake | Forecasting | A/B Testing | Feature Engineering | Big Data | Data VisualizationAt Abbott, I created trading risk models that improved portfolio accuracy by 18%. I automated ETL workflows using Airflow and SQL, deployed ML models on AWS, and used NLP to analyze financial news for faster trading decisions.At UnitedHealth Group, I built models that cut hospital readmission rates by 17% and improved targeting for chronic conditions by 21%. I worked with large-scale health data using PySpark and AWS Glue, and created Tableau dashboards to help leadership monitor cost drivers.At Citibank, I designed fraud detection and credit risk models that improved customer profiling. I enhanced data pipelines using SQL and Redshift, applied NLP to customer feedback, and built Tableau dashboards that guided compliance and marketing efforts.At FedEx, I predicted customer churn using TensorFlow and Spark. I streamlined data ingestion with AWS Glue and Spark, and applied clustering methods to detect anomalies faster. My work made reporting faster and insights more reliable.At AT & T, I created real-time fraud systems for payment transactions. I migrated legacy scripts to AWS Lambda and S3, cutting infrastructure costs by 20%. I also used A/B testing and NLP to improve product feedback analysis and delivered dashboards using Looker and Power BI.I specialize in solving complex business problems through data-driven solutions. I turn complex information into clear answers. I focus on impact, speed, and building systems that last.Contact Information: Email: s••••••••@gmail.comPhone:+12•••••••80Location: United States

Experience

1. Senior Data Scientist Ml Python Sql Apache Spark Airflow Nlp Power Bi Scikit-learn

   Abbott

   Sep 2024 — Present · Chicago, IL, US

   Developed predictive models using Python, SQL, and Scikit-learn to improve trading strategies, enhance forecasting, and boost overall model accuracy.2. Refined portfolio risk models, increasing predictive precision by 18% and directly supporting more profitable investment decisions.3. Interrogated multi-terabyte financial datasets using Pandas and Apache Spark to uncover real-time insights into market behavior.4. Automated complex ETL workflows with Apache Airflow and SQL, cutting processing time by 35% and improving data reliability.5. Translated business needs from quants and traders into scalable ML solutions by building analytical frameworks in R and Tableau.6. Executed A/B testing strategies that improved algorithmic trading performance by 12%, validating results through statistical rigor.7. Engineered NLP pipelines to extract sentiment from financial news and social media, strengthening event-driven trading decisions.8. Constructed real-time Power BI dashboards to automate reporting, reduce manual workload by 40%, and deliver executive-ready insights.9. Validated data streams using Python-based anomaly detection to enforce compliance standards and ensure clean, reliable datasets.10. Integrated ML workflows across AWS environments, reducing model deployment time by 22% and accelerating trading responses.11. Extracted and integrated alternative data (macro indicators, online signals) to strengthen predictive model performance.12. Reengineered ETL processes with Spark and SQL, reducing latency and supporting faster delivery of risk intelligence reports.13. Segmented client profiles using unsupervised learning techniques, enabling personalized recommendations and raising retention by 10%.14. Monitored live model drift and performance, applying updates to maintain accuracy and compliance with industry standards.15. Built and deployed Kafka-integrated anomaly detection systems using Scikit-learn to flag market irregularities in real time.