Megan King

• Phone: +1 416 555 0123
• Email: megan.king@email.com
• Location: Toronto, Canada
• LinkedIn: megan-king-ds

Summary

Led the development and deployment of predictive models that increased customer retention by 15% for a major e-commerce platform. Possessing 7 years of experience in end-to-end data science project lifecycle, from problem definition and data acquisition to model deployment and monitoring. Proficient in MLOps best practices, ensuring scalable and maintainable machine learning solutions across diverse industry sectors.

Experience

Senior Data Scientist, Maple Analytics -- Toronto, Canada

Mar 2021 – present

• Designed and implemented a real-time fraud detection system using deep learning, reducing fraudulent transactions by 20% and saving the company an estimated $2M annually.

• Developed and maintained MLOps pipelines using AWS SageMaker and Kubeflow, improving model deployment efficiency by 30% and reducing inference latency by 15%.

• Mentored junior data scientists on model development, validation, and productionization best practices, fostering a collaborative and high-performing team environment.

• Conducted A/B testing and statistical analysis to evaluate the impact of new features and models, providing data-driven recommendations for product improvements.

Data Scientist, NorthPeak Solutions -- Toronto, Canada

June 2018 – Feb 2021

• Built and validated machine learning models for customer churn prediction, achieving an AUC score of 0.88 and informing targeted retention strategies.

• Performed extensive exploratory data analysis and feature engineering on large datasets (1TB+) using PySpark and SQL to identify key drivers of business outcomes.

• Collaborated with engineering teams to integrate machine learning models into existing production systems, ensuring seamless deployment and monitoring.

• Presented complex analytical findings to non-technical stakeholders, influencing strategic business decisions and product roadmaps.

Junior Data Scientist, Bayview Data Labs -- Toronto, Canada

Sept 2016 – May 2018

• Assisted in the development of predictive models for market trend analysis, contributing to a 5% increase in investment portfolio returns.

• Cleaned, transformed, and validated diverse datasets from various sources to ensure data quality and integrity for analytical projects.

• Created data visualizations and dashboards using Tableau and Power BI to communicate insights effectively to internal teams.

• Participated in research and experimentation with new machine learning algorithms and techniques to enhance model performance.

Education

University of Toronto, M.Sc. in Master of Science in Computer Science (Specialization in Artificial Intelligence) -- Toronto, Canada

Sept 2015 – Aug 2016

McMaster University, B.Sc. in Bachelor of Science in Statistics and Computer Science -- Hamilton, Canada

Sept 2011 – May 2015

Skills

Programming & Tools: Python (Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch), R, SQL, Spark, Git, Docker, Kubernetes

Machine Learning: Supervised & Unsupervised Learning, Deep Learning, Reinforcement Learning, NLP, Time Series Analysis, Model Selection, Hyperparameter Tuning

MLOps & Cloud Platforms: AWS (SageMaker, S3, EC2, Lambda), Google Cloud Platform (Vertex AI, BigQuery), Azure ML, Kubeflow, MLflow, CI/CD

Data Warehousing & Databases: PostgreSQL, MySQL, MongoDB, Snowflake, Data Lake, Data Mart

Data Visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI

Statistical Analysis: Hypothesis Testing, Regression Analysis, A/B Testing, Bayesian Statistics, Causal Inference