Machine Learning Data Scientist
Resume:
Programming Languages:
Python, R, Java, SQL, Scala
Machine Learning
Algorithms: Gradient
Boosting, Decision Trees,
Random Forest, SVM, KNN,
KMeans, XGBoost,
LightGBM
Machine Learning
Frameworks: TensorFlow,
Keras, PyTorch, Scikit-learn
Natural Language
Processing (NLP): NLTK,
SpaCy, Gensim
Deep Learning: CNNs, RNNs,
LSTMs
Data Manipulation and
Visualization: Pandas,
NumPy, Matplotlib, Seaborn,
Tableau, Data Cleaning,
Data Wrangling, Feature
Engineering
Big Data Technologies:
Hadoop, Spark
Cloud Computing: AWS,
Azure
Containerization and
Orchestration: Docker,
Kubernetes
Recommender Systems
Time Series Forecasting
MUHAMMAD EBAAD
W O R K E X P E R I E N C E
I am an experienced data scientist with 14 years of experience in advanced machine learning and data science. Expertise in developing, deploying, and optimizing machine learning models in the healthcare, finance, and retail sectors. Proficient in Python, R, Java, and SQL, with strong skills in data preprocessing, feature engineering, and model evaluation. Specializes in computer vision, NLP, and recommender systems, leveraging frameworks like TensorFlow, PyTorch, and Scikit-learn. Adept at implementing scalable ML pipelines using Docker, Kubernetes, and cloud platforms such as AWS. Proficient in big data technologies like Hadoop and Spark, with a proven track record of transforming complex data into actionable insights, driving impactful business outcomes. My proven track record in reducing costs, improving accuracy, and enhancing customer engagement makes me a valuable asset for innovative, data-driven projects. EVERNOTE
Data Scientist
Aug 2020 - Current
At EVERNOTE, I’ve been deeply involved in enhancing our algorithms to better predict user needs and preferences. My work has directly contributed to a 25% increase in customer retention by ensuring that our product continually meets and exceeds user expectations. I analyze large vAolumes of data to uncover insights that inform strategic decisions, helping to shape and improve the features and functionality of our product. My role also includes designing and managing machine learning models that assist in personalizing recommendations and understanding user behavior. I collaborate closely with the engineering and product teams to integrate these data-driven insights into our systems, leading to more efficient processes and a 15% reduction in operational costs. Additionally, I oversee A/B testing for new features to assess their impact, which has resulted in a 30% increase in user engagement.
S K I L L S
C O N T A C T
muhammadebaad36@gmailcom
Newark NJ, 07101
FORWARD
Machine Learning Engineer
Jun 2015 - Aug 20
During my time at FORWARD, I focused on developing and deploying machine learning models specifically for healthcare applications. This work significantly enhanced diagnostic accuracy and improved patient care. I built and maintained data pipelines to ensure that data flowed smoothly from various sources into our machine learning models. My role also involved feature engineering, which improved the performance of our models and increased accuracy for key business metrics by 20%. I worked closely with a team of data scientists, engineers, and healthcare professionals to implement AI-driven solutions that added value to our services. I also introduced new machine learning techniques and tools that reduced model training time by 35% and improved overall system efficiency by 40%.
https://www.linkedin.com/in/muhamm
adebaad-9356bb228/
L A N G U A G E S
English (Fluent) Technologies Used: Python, Scikit-learn, XGBoost, TensorFlow Collected patient data, including medical history, symptoms, and lab results from electronic health records (EHRs).
I cleaned and preprocessed the data using Pandas, addressing missing values and normalizing features.
Developed features based on symptom patterns, lab results, and demographic information.Applied machine learning models using Scikit-learn and XGBoost, and deep learning models using TensorFlow for disease prediction.
Evaluated model performance using metrics like accuracy, precision, recall, and F1-score. Integrated the disease prediction model into the healthcare system to assist doctors in early diagnosis and treatment planning.
Fraud Detection (Fintech)
Technologies Used: Python, Scikit-learn, TensorFlow, Apache Kafka Collected transaction data including payment history, account details, and user behavior patterns.I cleaned and preprocessed the data using Pandas, handling missing values and normalizing transaction amounts.
Created features such as transaction frequency, amount patterns, and user location.
Applied anomaly detection techniques using Scikit-learn and deep learning models with TensorFlow to identify fraudulent transactions. Evaluated model performance using metrics like true positive rate
(TPR) and false positive rate (FPR).
Deployed the fraud detection system with real-time processing capabilities using Apache Kafka to monitor transactions continuously. P R O J E C T S
Anomaly Detection
Computer Vision
Model Evaluation Metrics:
Accuracy, Precision, Recall,
F1-score, True Positive Rate
(TPR), False Positive Rate
(FPR)
E D U C A T I O N
Fairleigh dickinson University
2006 - 2010
BS computer science
SERACLE
Data Analyst
Jan 2011 - Jun 2015
At SERACLE, I was responsible for creating interactive dashboards and visualizations that provided clear, actionable insights for our business stakeholders. These tools were essential in helping various departments make informed decisions. I took great care in ensuring that our data was clean and accurate, which helped to cut reporting errors by 50%. My analyses of data sets helped identify trends and patterns that were crucial for shaping the company’s strategy and growth. I also produced detailed reports that summarized key findings and offered recommendations, which supported senior management in their decision-making processes. In addition, I introduced new data analysis tools and methods, which made the data processing and analysis 30% more efficient.
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