Faima B

Senior Data Scientist and Machine Learning Engineer with 10+ years of experience designing, developing, deploying, and monitoring production machine learning systems, analytics solutions, and AI applications. Experienced across the complete ML lifecycle including data analysis, feature engineering, predictive modeling, experimentation, model evaluation, deployment, monitoring, and continuous improvement. Strong background in Python, SQL, R, machine learning, deep learning, natural language processing, large language models (LLMs), retrieval-augmented generation (RAG), embeddings, cloud-based ML workflows, MLOps, and distributed data processing.

Experienced building scalable data pipelines, production ML APIs, automated workflows, and business intelligence solutions using AWS, Azure, GCP, Databricks, Spark, Docker, Kubernetes, and MLflow. Proven ability to translate ambiguous business problems into measurable analytical solutions, communicate complex findings to technical and non-technical stakeholders, mentor engineering teams, and deliver reliable AI systems that create measurable business impact.

Senior Data Scientist / Machine Learning Engineer - EPAM Technology

(2022-09)

• Lead end-to-end machine learning initiatives, developing production ML systems, predictive models, AI applications, and scalable analytical solutions for enterprise customers.
• Manage and mentor a team of six data scientists, machine learning engineers, and developers across model development, experimentation, code reviews, deployment, monitoring, and technical delivery.
• Design, train, validate, and deploy machine learning models using Python, SQL, Scikit-learn, XGBoost, PyTorch, TensorFlow, and cloud-based ML platforms.
• Build Retrieval-Augmented Generation (RAG) applications using large language models, embeddings, vector databases, semantic search, and transformer-based architectures.
• Develop scalable ML pipelines using AWS, Databricks, Apache Airflow, MLflow, Docker, and Kubernetes to automate training, deployment, monitoring, and lifecycle management.
• Implement production ML best practices including experiment tracking, model versioning, automated validation, CI/CD workflows, monitoring, drift detection, and reliability improvements.
• Perform exploratory data analysis, statistical analysis, feature engineering, and model evaluation to identify business opportunities and improve decision-making.
• Collaborate with product managers, engineers, analysts, and business stakeholders to translate complex requirements into measurable AI and analytics solutions.
• Create analytical reports, dashboards, and technical documentation to communicate findings and recommendations across cross-functional teams.

Data Scientist / Machine Learning Engineer - Rackspace Technology

(2018-05 - 2022-08)

• Developed, deployed, and maintained machine learning models for customer analytics, cloud infrastructure forecasting, classification systems, anomaly detection, and operational intelligence.
• Analyzed large-scale datasets using Python, SQL, Amazon Redshift, AWS services, Apache Spark, and distributed processing frameworks to generate actionable business insights.
• Built predictive models using Scikit-learn, XGBoost, LightGBM, and statistical techniques including feature engineering, model selection, validation, and performance optimization.
• Created transformer-based natural language processing applications that classified and routed more than 500,000 support tickets annually, improving workflow automation and operational efficiency.
• Designed and maintained distributed data pipelines using Amazon S3, AWS Glue, Amazon Redshift, Apache Spark, and Apache Airflow processing approximately 2 TB of operational data daily.
• Conducted exploratory data analysis, statistical testing, hypothesis testing, A/B testing analysis, error analysis, and root-cause investigations to identify improvement opportunities.
• Developed production ML APIs and model-serving applications using FastAPI, Docker, Kubernetes, Jenkins, and CI/CD pipelines.
• Implemented MLflow experiment tracking and monitoring workflows for model performance, prediction quality, latency, feature health, and data drift detection.
• Created dashboards, analytical reports, and data visualizations using Tableau, Power BI, Matplotlib, and Plotly to communicate insights to stakeholders.
• Collaborated with data scientists, engineers, cloud architects, analysts, and business teams to deliver reliable machine learning solutions in fast-paced environments.

Associate Data Scientist - Effectual

(2015-11 - 2018-04)

• Supported machine learning and analytics projects through data preparation, exploratory data analysis, feature engineering, statistical analysis, and model validation.
• Developed foundational experience with regression, classification, clustering, time-series forecasting, Python programming, SQL analysis, and reproducible analytical workflows.
• Assisted with data cleaning, transformation, quality validation, and preparation of datasets for machine learning experiments.
• Collaborated with data scientists, engineers, analysts, and project stakeholders to deliver analytical solutions using Agile methodologies.
• Participated in model evaluation, documentation, testing, and knowledge-sharing activities to improve analytical processes.

Bachelor of Science