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Data Scientist | ML Engineer | Big Data & Product Analytics I am a Data Scientist who believes that the most powerful models are the ones that solve real-world product problems. From the precision-heavy world of pharmaceutical research to the high-scale environment of telecommunications, I specialize in building end-to-end data solutions that transform raw, terabyte-scale data into strategic business assets. What I bring to the table: Big Data Engineering: I have a proven track record of handling terabytes of daily data, architecting pipelines on Databricks using PySpark, SQL, and Scala. I am proficient in modern MLOps practices, including Databricks Asset Bundles (DABs) and CI/CD integration. Predictive Modeling & Research: I enjoy solving complex data challenges, such as benchmarking ML imputation methods for missing pharmaceutical data or building Anomaly Detection (Isolation Forests) and Time-Series churn models for telecom products. Product-Driven Insights: I bridge the gap between technical data and product strategy. I have built pattern detection engines to analyze API behavior and designed automated Tableau dashboards that empower Product Managers to make data-backed decisions. Technical Toolkit: Languages: Python, SQL, Scala, R. Data Engineering: Apache Spark (PySpark), Databricks, ETL/ELT, Avro. ML/Stats: Supervised/Unsupervised Learning, Pattern Recognition, Time-Series, Statistical Benchmarking. Visualization: Tableau, PowerBI, Matplotlib/Seaborn. I am always eager to connect with fellow data enthusiasts and product innovators. Let’s talk about how we can turn complex datasets into clear growth opportunities.…Data Scientist | ML Engineer | Big Data & Product Analytics I am a Data Scientist who believes that the most powerful models are the ones that solve real-world product problems. From the precision-heavy world of pharmaceutical research to the high-scale environment of telecommunications, I specialize in building end-to-end data solutions that transform raw, terabyte-scale data into strategic business assets. What I bring to the table: Big Data Engineering: I have a proven track record of handling terabytes of daily data, architecting pipelines on Databricks using PySpark, SQL, and Scala. I am proficient in modern MLOps practices, including Databricks Asset Bundles (DABs) and CI/CD integration. Predictive Modeling & Research: I enjoy solving complex data challenges, such as benchmarking ML imputation methods for missing pharmaceutical data or building Anomaly Detection (Isolation Forests) and Time-Series churn models for telecom products. Product-Driven Insights: I bridge the gap between technical data and product strategy. I have built pattern detection engines to analyze API behavior and designed automated Tableau dashboards that empower Product Managers to make data-backed decisions. Technical Toolkit: Languages: Python, SQL, Scala, R. Data Engineering: Apache Spark (PySpark), Databricks, ETL/ELT, Avro. ML/Stats: Supervised/Unsupervised Learning, Pattern Recognition, Time-Series, Statistical Benchmarking. Visualization: Tableau, PowerBI, Matplotlib/Seaborn. I am always eager to connect with fellow data enthusiasts and product innovators. Let’s talk about how we can turn complex datasets into clear growth opportunities.

Othmane Kounima

Data Scientist, AI Engineer, AI Developer, +5

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Data Scientist | ML Engineer | Big Data & Product Analytics I am a Data Scientist who believes that the most powerful models are the ones that solve real-world product problems. From the precision-heavy world of pharmaceutical research to the high-scale environment of telecommunications, I specialize in building end-to-end data solutions that transform raw, terabyte-scale data into strategic business assets. What I bring to the table: Big Data Engineering: I have a proven track record of handling terabytes of daily data, architecting pipelines on Databricks using PySpark, SQL, and Scala. I am proficient in modern MLOps practices, including Databricks Asset Bundles (DABs) and CI/CD integration. Predictive Modeling & Research: I enjoy solving complex data challenges, such as benchmarking ML imputation methods for missing pharmaceutical data or building Anomaly Detection (Isolation Forests) and Time-Series churn models for telecom products. Product-Driven Insights: I bridge the gap between technical data and product strategy. I have built pattern detection engines to analyze API behavior and designed automated Tableau dashboards that empower Product Managers to make data-backed decisions. Technical Toolkit: Languages: Python, SQL, Scala, R. Data Engineering: Apache Spark (PySpark), Databricks, ETL/ELT, Avro. ML/Stats: Supervised/Unsupervised Learning, Pattern Recognition, Time-Series, Statistical Benchmarking. Visualization: Tableau, PowerBI, Matplotlib/Seaborn. I am always eager to connect with fellow data enthusiasts and product innovators. Let’s talk about how we can turn complex datasets into clear growth opportunities.…Data Scientist | ML Engineer | Big Data & Product Analytics I am a Data Scientist who believes that the most powerful models are the ones that solve real-world product problems. From the precision-heavy world of pharmaceutical research to the high-scale environment of telecommunications, I specialize in building end-to-end data solutions that transform raw, terabyte-scale data into strategic business assets. What I bring to the table: Big Data Engineering: I have a proven track record of handling terabytes of daily data, architecting pipelines on Databricks using PySpark, SQL, and Scala. I am proficient in modern MLOps practices, including Databricks Asset Bundles (DABs) and CI/CD integration. Predictive Modeling & Research: I enjoy solving complex data challenges, such as benchmarking ML imputation methods for missing pharmaceutical data or building Anomaly Detection (Isolation Forests) and Time-Series churn models for telecom products. Product-Driven Insights: I bridge the gap between technical data and product strategy. I have built pattern detection engines to analyze API behavior and designed automated Tableau dashboards that empower Product Managers to make data-backed decisions. Technical Toolkit: Languages: Python, SQL, Scala, R. Data Engineering: Apache Spark (PySpark), Databricks, ETL/ELT, Avro. ML/Stats: Supervised/Unsupervised Learning, Pattern Recognition, Time-Series, Statistical Benchmarking. Visualization: Tableau, PowerBI, Matplotlib/Seaborn. I am always eager to connect with fellow data enthusiasts and product innovators. Let’s talk about how we can turn complex datasets into clear growth opportunities.

Available to hire

Antwerpen, Belgium

Data Scientist | ML Engineer | Big Data & Product Analytics

I am a Data Scientist who believes that the most powerful models are the ones that solve real-world product problems. From the precision-heavy world of pharmaceutical research to the high-scale environment of telecommunications, I specialize in building end-to-end data solutions that transform raw, terabyte-scale data into strategic business assets.

What I bring to the table:

Big Data Engineering: I have a proven track record of handling terabytes of daily data, architecting pipelines on Databricks using PySpark, SQL, and Scala. I am proficient in modern MLOps practices, including Databricks Asset Bundles (DABs) and CI/CD integration.

Predictive Modeling & Research: I enjoy solving complex data challenges, such as benchmarking ML imputation methods for missing pharmaceutical data or building Anomaly Detection (Isolation Forests) and Time-Series churn models for telecom products.

Product-Driven Insights: I bridge the gap between technical data and product strategy. I have built pattern detection engines to analyze API behavior and designed automated Tableau dashboards that empower Product Managers to make data-backed decisions.

Technical Toolkit:

Languages: Python, SQL, Scala, R.

Data Engineering: Apache Spark (PySpark), Databricks, ETL/ELT, Avro.

ML/Stats: Supervised/Unsupervised Learning, Pattern Recognition, Time-Series, Statistical Benchmarking.

Visualization: Tableau, PowerBI, Matplotlib/Seaborn.

I am always eager to connect with fellow data enthusiasts and product innovators. Let’s talk about how we can turn complex datasets into clear growth opportunities.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

French

Fluent

English

Fluent

Arabic

Fluent

Spanish; Castilian

Advanced

Dutch

Intermediate

Work Experience

Data Scientist at Airties

February 1, 2022 - Present

-> Building end-to-end ML solutions (anomaly detection, churn prediction) providing proactive solutions to the business. (Millions of £ saved in customer lifetime value gain) -> Big data crunching into insightful BI dashboards to assist Product management decision making (Pyspark, SQL, Tableau) -> AVRO raw data (10 sec granularity) processing into insightful aggregates, in order to conduct a statistical analysis (A/B testing, inference) -> API logs analysis, pattern detection using Regex, in order to build a dashboard for Product management and sales people to track deployment KPIs.

Machine Learning Intern at GSK

March 1, 2021 - August 1, 2021

-> Litterature review for missing values imputation methods -> End-to-end Python pipeline to benchmark multiple solutions -> Report to head of R&D to improve clinical trials