Maxime Leroux

Background

About Maxime Leroux

Mr. Leroux is an experienced leader in machine learning (ML), artificial intelligence (AI), big data technologies, and software engineering with a proven track record in developing innovative solutions to solve complex challenges across industries. His expertise spans sectors such as healthcare, finance, fraud detection, client engagement, process automation, credit and market risks, and marketing analytics. In his career, Mr. Leroux has designed and deployed advanced human-in-the-loop automated predictive modeling systems, recommendation engines, fraud detection algorithms, and behavioral analytics models. He has also developed robust systems for credit risk assessment, market risk management, portfolio optimization, and ad campaign targeting. His work on data-driven decision-making and personalization has enabled companies to optimize customer experiences and enhance business outcomes. A strong advocate of leveraging AI and machine learning at scale, Mr. Leroux has extensive experience leading cross-functional teams to build cloud-based infrastructures and implement big data processing solutions. His technical proficiency includes cloud platforms and data architecture, both on-premises and in the cloud, with a focus on Google Cloud Platform (GCP) and Microsoft Azure. Mr. Leroux has also provided strategic guidance in areas such as technology assessment, data science governance, and ethical AI practices. Programming skills - Advanced : Python, Shell (Linux), SQL - High :(Py)Spark, R, SAS - Others : Matlab, Scala, C# Distributed Tools - Spark (core, ML, Graph, Streaming) Visualization & Dashboards - Power BI, Tableau, Dash, Data stores - PostgreSQL, mySql, SQL Server, mongo, Cassandra, Chroma, Redis Courses & Trainings - Introduction to Data Science (Cloudera). Data acquisition & transformation, statistical analysis and machine learning in Hadoop- Developer Training for Apache Spark (Cloudera). Development of Spark Applications : Spark, Spark Streaming, Spark SQL, GraphX, MLlib - Data Science with Apache Spark (Databricks - Spark Summit East 2016). In depth review of Spark's ML & MLlib libraries- Exploring Wikipedia with Spark 2.x (Databricks - Spark Summit East 2017). In depth review of Spark 2.x