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Lucas M.

Machine Learning Engineer

Lucas is a skilled Data/ML Engineer with over five years of experience in data engineering, data science, machine learning, and Python.

He has a proven track record of designing and implementing various ML and data pipelines, including building a data platform from scratch. With experience working in healthcare, real estate, education, and marine engineering, Lucas is a curious and excited generalist who is always eager to learn new things.

He is keen to work for companies where data or artificial intelligence plays a crucial role.

Currently, Lucas is part of an early-stage health-tech company focused on addressing overweight and obesity issues in Latin America.

Main expertise
  • Data Engineering 2 years
  • Data Science 4 years
  • Docker
    Docker 3 years
Other skills
  • Matlab
    Matlab 2 years
  • PostgreSQL
    PostgreSQL 2 years
  • Scrum
    Scrum 2 years
Lucas M.


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Selected experience


  • Senior Data Engineer

    Liti - 2 years 4 months

    • Part of the founding team built the company's highly scalable, robust data platform using Airflow, Fivetran, DBT and BigQuery, cloud-based in GCP;
    • Using dbt, built fully traceable, transparent transformation layers (raw/stg/int/mart) that turned data from Hubspot, Calendly, Typeform, and MongoDB into intuitive, insightful data models;
    • Using Python and Airflow, developed and orchestrated data pipelines for core business data sources, such as Calendly and Google Sheets;
    • Built an EHR system that uses Python, Kubernetes, and Cloud SQL and integrates with BigQuery;
    • Reduced health professionals' time spent looking for data in 30 minutes per appointment.


    • Technologies:
    • Apache Airflow Apache Airflow
    • Data Engineering
    • Docker Docker
    • Git Git
    • Google Cloud Google Cloud
    • Kubernetes Kubernetes
    • MongoDB MongoDB
    • Python Python
  • Data Scientist

    Loft - Top Brazilian Unicorn startup - Lunicorn - 8 months

    • Achieved a series D valuation of 3 billion USD.
    • Developed several ETL processes in the company's large-scale data warehouses using Python, Pandas, Databricks, and Spark.
    • Conducted A/B tests using 100GBs of navigation data to determine the impact of recommendation models on company sales.
    • Optimized a Python Machine Learning recommender system for real estate property visits using bounding box policies, resulting in a 15% uplift in visit schedules.
    • Built a multiple regression system using Python, Pandas, Streamlit, and Plotly to allocate real estate brokers across three of Brazil's largest cities, minimizing visit overbookings by over 30%.
    • Constructed a sales forecast using a SARIMA model.


    • Technologies:
    • Data Science
    • Git Git
    • Machine Learning Machine Learning
    • Pandas Pandas
    • Python Python
  • Data Scientist and Instructor

    Ada Tech - 1 year

    • Created an entire BI platform for the company using Python, Pandas and Streamlit;
    • Developed lots of ETL processes, integrating with the company's data warehouse;
    • Led two end-to-end data projects;
    • Built data pipelines, data models, live analytics dashboards, machine learning models, CI/CD processes and A/B tests, all AWS native, using Lambda, S3, and Code (Commit, Build, Pipeline);
    • Worked on machine learning-based sales, scoring the system that leverages data from sources such as WhatsApp and Google Analytics;
    • Developed a recommender system used to find the questions that best fit the studying pattern of a given student, using Reinforcement and Unsupervised Learning.


    • Technologies:
    • Matlab Matlab
    • Git Git
    • Machine Learning Machine Learning
    • Pandas Pandas
    • Python Python
  • Reinforcement Learning Researcher

    University of São Paulo - 1 year 1 month

    • Co-author of AI Research for large ships in the real world, restricted environmental and maneuvring conditions;
    • Designed experiments and modeled data representations for training state-of-the-art Asynchronous Deep Reinforcement Learning algorithms using Python, Tensorflow and Redis;
    • Developed a robust RL training infrastructure based on Docker that provides scalability and reliability for the heavy Machine Learning operations on the local cluster;
    • Created visualization and testing tools for the terabyte, week-long AI training sessions, which allowed significant research advancements.


    • Technologies:
    • Docker Docker
    • Machine Learning Machine Learning
    • Python Python
    • TensorFlow TensorFlow


  • BSc.Electrical Engineering

    Escola Politécnica da USP · 2017 - 2021


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