This journey illustrates how numeracy, curiosity, and persistence can shape a modern data career, evolving from technical foundations to leadership in the business world. For example, he has developed five products from scratch, including fraud detection, personalization, and optimization, covering proof of concept, deployment, and monitoring.
He is a Senior Data Scientist
He has a Master’s in Finance and a PhD in Statistics. His expertise includes statistical modeling, data science, machine learning engineering, optimization models, consumer science, and risk modeling.
Career path
An early immersion in statistical reasoning taught him to think rigorously about causality, correlation, and model validity. Transitioning to industry as a data analyst, he collaborated with process and operations teams on practical business applications. At a leading e-commerce company in South America, he quickly advanced to become a Data Scientist and later a Machine Learning Engineer, developing pricing optimization models, behavioral models, and recommender systems. He coded in Python, utilizing libraries such as NumPy, Pandas, and TensorFlow, and managed end-to-end pipelines on Google Cloud Platform (GCP).
After moving to Europe, he joined a global delivery platform where he worked on customer identity resolution, incentive optimization, and semi-real-time recommendation systems across multiple countries. This involved balancing data science with engineering and aligning with product goals, using tools such as BigQuery, Airflow, and CI/CD deployment.
Ready as a Lead Data Scientist
Over the past years, he has delivered more than ten products, typically coordinating 2-4 teams, comprising 10-30 people in total. As a technical leader, he builds and operates global data platforms (AWS/GCP), working closely with senior business executives to address both the pressing and promising data needs.
Breadth of competence
He has built a broad T-shaped skill set that covers statistics, machine learning, data modeling, and leadership. This diverse skill set allows him to move seamlessly between technical and strategic roles — from building a model from scratch to deploying it and explaining its business impact to executives.
His competence includes, for example,
Cloud platforms: GCP and AWS for data storage, model training, and deployment.
Data infrastructure: BigQuery, Teradata, Hive, Presto, and ETL orchestration.
Programming and modeling: Python (NumPy, Pandas, Scikit-Learn, TensorFlow), SQL, and probabilistic modeling.
Product analytics: Creating ML products aligned with business objectives — from fraud detection and behavioral analysis to recommender systems and incentive optimization.
Business analytics: Setting metrics for ROI, customer segmentation, and fraud risk, combining business insights with statistical rigor.
Leadership: Planning roadmaps, mentoring team members, and promoting cross-functional collaboration among executives, analysts, and engineers.
Depth of expertise
As a lead, he exemplifies the increasing maturity of the data profession, balancing statistical thinking, machine learning, and business acumen.
His expertise lies in parsimonious and production-ready models that
Scale effectively across regions and product lines,
Integrate smoothly into live systems,
Are monitored and improved through feedback loops,
Generate measurable ROI.
He also develops annual and quarterly roadmaps for the data department, liaises with executives, translates business needs into technical solutions, and oversees hiring and mentoring of teams.
Questions for the reader
If you have read this far, you may have questions or comments. Please leave them below, and I will respond.
Additionally, I have questions for you.
How much have you invested in the numerical foundations of your career, particularly through education and before joining industry?
How do you imagine the transition from a technical role (e.g., Lead, Principal) to a role in business leadership (e.g., Director)?
Building your competency profile
Suppose you are recognized as a Senior or have 3+ years of experience in the field. In that case, you can build your competency profile to advance your career to Lead, Director, and Principal. You can choose
In-person day workshop (e.g., Berlin, or another European data metropolis) with a maximum of ten peers, and an individual follow-up to review your final draft competency profile and action plan.
Online 1-to-1 coaching (anywhere) with dedicated support until your competency profile is complete and ready for use for your promotion talk.
AI Guild
AI Guild members are experts and leaders in Data & AI, e.g., Analytics Engineering, Business Intelligence, Computer Vision, Data Analytics, Data Engineering, Data Science, Deep Learning, Machine Learning, MLOps, NLP, and Prompt Engineering.
