Open to opportunities

Naoufal Acharki

@naoufalacharki

Message

I am a senior data scientist specializing in causal inference, ML, and MLOps.

France

Message

What I'm looking for

I seek senior roles leading end-to-end ML products where I apply causal inference and MLOps to solve industry problems (energy, marketing, fintech) within collaborative, impact-driven teams.

I hold a Ph.D. in Applied Mathematics and have six years' experience as a senior data scientist focused on statistical modeling, causal inference, and production machine learning across energy, marketing, and fintech.

I design and deploy end‑to‑end ML systems and pipelines—building recommendation engines (CampAI) that increased conversion uplift by up to 30%, an AI financial assistant, productionized microservices on cloud infrastructure, and automated feature engineering and ETL processes.

I publish research on causal meta‑learners and Gaussian processes (ICML and Computational Statistics), and I lead cross‑functional teams to deliver measurable results while emphasizing reproducibility, MLOps best practices, and clear stakeholder communication.

Experience

Work history, roles, and key accomplishments

Co-founder and Chief Technology Officer

Kemba

Jan 2025 - Aug 2025 (7 months)

Developed an AI financial assistant orchestrating three specialized AI agents (fraud detection, proactive budgeting, investment advisor) and built portfolio dashboards to improve user decision-making. Orchestrated full‑stack AWS infrastructure (EC2, Redshift), implemented GitHub Actions CI/CD and CloudWatch monitoring to deploy the service to production.

Orchestration Fraud Detection AWS EC2 Amazon Redshift GitHub Actions)CI CD Cloudwatch

Research Intern

TotalEnergies OneTech

Mar 2019 - Sep 2019 (6 months)

Developed a Gaussian process regression model in R to predict gas well production with 80% confidence and conducted sensitivity analyses to identify key predictive factors. Applied Sobol, Shapley and HSIC methods within quasi‑experimental designs to inform model robustness and feature importance.

R Gaussian Processes Sensitivity Analysis Sobol Shapley HSIC Geostatistics

Engineering Intern

TotalEnergies OneTech

Jun 2018 - Sep 2018 (3 months)

Applied Topological Data Analysis (TDA Mapper) and compared it with unsupervised learning methods to cluster well data, demonstrating TDA's pattern‑detection capabilities. Produced comparative results to guide downstream clustering and feature engineering for well datasets.

Topological Data Analysis TDA Mapper Unsupervised Learning Clustering Data Analysis Python R

Education

Degrees, certifications, and relevant coursework

ÉP

École Polytechnique, Institut Polytechnique de Paris

Doctor of Philosophy, Applied Mathematics (Statistics and Machine Learning)

2019 - 2022

Ph.D. in Applied Mathematics specializing in statistics and machine learning; research focused on causal inference and heterogeneous treatment effects with work accepted at ICML 2023.