Our pipeline
Mauricio Lelis
LINKEDIN VERIFIEDLINKEDIN PREMIUMData scientist / ML engineer
Brazil LinkedIn · 1.3k followers · on LinkedIn since 2008
About
web sources + resumeWeb sources + resume claims.
Copied from LinkedIn — not independently verified.
Report
professional-data verificationHiring assessment
6 role-fits · 2 risksLLM judgment on best-fit roles, bounded by available evidence.
Also a strong fit
7+ years in data science/ML across ACP Group and Nestlé, with hands-on Azure ML, Databricks and MLflow CI/CD experience, fit roles that require building and operationalizing models at scale. Latin America big-data context and stakeholder communication at Nestlé align with senior IC roles and customer-facing solution architect work.
Risks & gaps
Work Experience
13 confirmationsWeb sources + resume claims.
Photo gallery
Card images from confirmed sources where the subject is featured. Click any tile to open the source page. Not face- verified — some thumbnails may be article hero shots that show another person.
Personal sites
1 siteWeb properties the subject controls — blog, bio domain, project landing page.
- Personal websitetheorg.com
Digital channels
1 linkSubject-owned online presence aggregated from LinkedIn, resume, and personal-signal mentions.
- LinkedInin/mjlelis
LinkedIn posts
86 posts · 2019-08-01 → 2025-11-07 · 93 107 reactions · 1 326 comments — overview, then top by engagement.
Posts focus on data science, machine learning/AI and applied ML (including healthcare), cloud engineering and certifications (GCP, AWS, Azure, Databricks, Terraform), plus Nestlé corporate updates and sustainability/product announcements. There are 86 posts spanning 2019-08-01 to 2025-11-07, with steady activity across the period and notable posting bursts and high-visibility content in 2023–2024. Engagement is skewed: overall totals are 93,107 reactions and 1,326 comments, but most individual posts get modest (single- to low-double-digit) reactions while a handful of corporate/sustainability posts in 2023–2024 attracted very large engagement (10k–31k+ reactions).
Education
1 institutionCopied from LinkedIn — not independently verified.
Notable quotes
Inbound mentions
1 mentionVerbatim quotes about the subject from third-party sources.
General mentions
1 of 1 unsigned mentions (no specific author attributed).
Reputation signals
Synthesised from web sources.
- Listed by The Org as a Senior Data Scientist at Nestlé (Brazil). theorg.com 2medium proof
Beyond the resume
Copied from LinkedIn — not independently verified.
- Lists English as “Professional working proficiency.” br.linkedin.com 3medium proof
Historical traces
Earlier-era artifacts: old blogs, Wayback finds, discontinued projects.
2010-2015
Sources
3 cited- 1linkedin.com
- 2
- 3br.linkedin.com
Resume + LinkedIn (verbatim, LLM-organized)
MAURICIO LELIS
Data Scientist / Machine Learning Engineer · Cloud Professional Specialist
+55 71 992385072 · mjlelis@gmail.com · linkedin.com/in/mjlelis/ · Brazil
Bio
Specialist in solutions that enhance Data Driven results with a focus on Data Science, Machine Learning Engineering, MLOps and LLMOps. Extensive experience in building and maintaining ML infrastructure, automating workflows, and implementing CI/CD processes for ML models. Specialties: Data Analysis and Cloud Data-Driven Management, Data Engineering, Machine Learning Engineering, MLOps, LLMOps, Project Management (Scrum and PMBok). Technologies: Scala, Python, Pandas, NumPy, Scikit-Learn, spaCy, Deep Learning, Reinforcement Learning, LLMs, NLTK, TensorFlow, Keras, PyTorch, PySpark, Snowflake, Databricks, ETL/Pipelines, Neo4j Graph Data Science, GCP ML Resources (Dataflow, Vertex AI, etc.), Azure ML Stack, MLFlow, TFX, Git, Airflow, Docker, Kubernetes, Power BI, Tableau, Streamlit, Flask, Django, ReactJs.
PROFESSIONAL EXPERIENCE
- Lead MLOps and LLMOps initiatives to streamline machine learning lifecycle management, from development to production deployment
- Design and implement end-to-end ML pipelines with automated CI/CD workflows using tools like MLflow, Airflow, and Kubernetes
- Develop and maintain infrastructure for Large Language Model operations (LLMOps), including prompt engineering, fine-tuning, and deployment strategies
- Build monitoring and observability systems for ML models in production, tracking performance metrics, data drift, and model degradation
- Implement containerization strategies using Docker and orchestration with Kubernetes for scalable ML workloads
- Create automated testing frameworks for ML models, ensuring reliability and reproducibility across environments
- Collaborate with Data Science teams to operationalize research models and optimize them for production efficiency
- Establish MLOps best practices and governance frameworks, including model versioning, experiment tracking, and documentation standards
- Optimize cloud infrastructure costs while maintaining high performance and availability for ML systems across AWS, Azure, and GCP
- Develop LLM-powered applications for retail operations, including customer service automation, product recommendations, and inventory optimization
- Implement feature stores and data versioning systems to ensure consistency and traceability in ML pipelines
- Lead technical workshops and knowledge sharing sessions on MLOps and LLMOps practices with cross-functional teams
- Participated in A/B testing experiments to evaluate competing ML models for product recommendation and personalization; designed experiment frameworks to measure lift in click-through and conversion rates, enabling statistically grounded model promotion decisions in production
- Participated in scrums and delivered all objectives within defined scope including priority definition
- Delivered AA prototype products (pilots) in identified high-value areas, scaling to deliver business value
- Designed, built, and maintained scalable and reliable ML infrastructure and pipelines including data ingestion, feature engineering, model training, validation, deployment, and monitoring
- Automated ML workflows to ensure efficiency, reproducibility, and consistency across the ML lifecycle
- Implemented and managed CI/CD processes for ML models
- Developed monitoring and alerting systems to track model performance, data drift, and system health in production
- Collaborated with Data Scientists and ML Engineers translating needs into robust and scalable infrastructure solutions
- Ensured security, reliability, and performance of ML systems
- Implemented best practices for ML governance, compliance, and auditability
- Optimized ML models and infrastructure for performance and cost-efficiency
- Managed and orchestrated containerized ML applications using Docker and Kubernetes
- Worked with cloud platforms (AWS, Azure, GCP, Databricks) and their ML services
- Participated in A/B testing initiatives to evaluate dynamic pricing models across distribution channels; collaborated with analytics and commercial teams to design controlled experiments, define success metrics, and interpret results — supporting data-driven pricing strategy decisions
- Participated in each stage of the data science lifecycle, from business understanding through model development to retail and industry processes
- Designed, built and maintained statistical/machine learning models to optimize business operations with Azure DevOps, Azure Machine Learning, Databricks, Python, PySpark, SQL and MLFlow
- Implemented MLOps practices with Azure and Databricks integration for CI/CD in Big Data scenarios across Latin America
- Explored and experimented with innovative ML techniques to maximize value generated by the team
- Developed methodology within large-scale transformational projects
- Communicated model and analysis results to non-technical audiences including stakeholders and senior management
- Supported A/B testing experiments to assess the effectiveness of B2B recommendation bundles; worked alongside commercial and data teams to structure test/control groups, track adoption metrics, and validate the incremental impact of ML-driven product bundle suggestions on B2B client ordering behaviour
- Worked directly with Data Driven and Business teams to understand customer needs
- Strategized on cloud data use cases providing compelling value-based demonstrations
- Developed and optimized machine learning models
- Architected delivery pipelines with hands-on coding
- Supported enterprise Proof of Concepts applying data governance, data engineering and ML engineering techniques
- Acted as strategic partner in closing business deals
- Responsible for evaluating market opportunities in a data-driven point of view, with emphasis on the needs of people and businesses, and making recommendations for modeling and strategic changes of products and services using best practices, aiming to minimize risks and increase revenues.
- Teach at Introductory interaction design course and UX workhops.
- Working in processes of analyzing broad and complex scenarios, using strong principles of usability and design methodologies, emphasizing users and stakeholders requirements, with the aim of to execute design deliverables of digital interactive projects; transmedia or cross plataform products.
- Noting the balance between real resources and the fundamental requirements of inovation and quality, the best professional stage to acting is in jobs with dynamic, proactives and interactive teams.
EDUCATION
TECHNICAL SKILLS
MLOps & LLMOps: MLflow, TFX, Airflow, Docker, Kubernetes, CI/CD, Model Monitoring, Feature Stores
Cloud Platforms: GCP (Vertex AI, Dataflow, BigQuery ML), AWS, Azure (ML Studio, Databricks)
Programming: Python, Scala, PySpark, SQL
ML Frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn
Data Engineering: Databricks, Snowflake, ETL Pipelines, Data Processing
LLMs & NLP: Prompt Engineering, Fine-tuning, NLTK, spaCy
Visualization: Power BI, Tableau, Streamlit
Methodologies: Agile, Scrum, PMBok
Experimentation: A/B Testing, Experiment Design, Statistical Hypothesis Testing, Lift Measurement
KEY CERTIFICATIONS
KEY ACHIEVEMENTS
Enhanced Model Accuracy: Increased model accuracy by 20% using ensemble techniques
Operational Cost Reduction: Reduced operational costs by 15% through optimized data workflows
Boosted Sales Revenue: Implemented ML models increasing sales by $00K yearly
Data Pipeline Automation: Automated data pipeline saved 30% processing time
LANGUAGES
English: Professional working proficiency
Portuguese: Native
Projects
- Pesquisa, Arquitetura de Informação e Design de Interação.