Data Science with 2+ years in Machine Learning & Time Series Forecasting
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Assessing your cultural and operational fit
Data Scientist with 2+ years of experience building ML models, time series forecasting systems, churn prediction, credit scoring, and recommendation engines in production. Proficient in Python and SQL. Skilled in data mining, anomaly detection, and communicating findings to technical and non-technical stakeholders in Agile environments.
Ecole des Sciences de l'Information (ESI)
State Engineer's Degree · Data & Knowledge Engineering
August 1, 2020 – June 30, 2023
CPGE
Preparatory Classes for Engineering Schools (MP/MPSI) · MP/MPSI
August 1, 2018 – June 30, 2020
OMTPME
Data Scientist
February 1, 2024 – Present
Casablanca, Casablanca-Settat, Morocco
CMAIS
Data Scientist
February 1, 2023 – August 1, 2023
Rabat, Morocco
Recommendation System
June 8, 2026 – Present
Hybrid collaborative + content-based filtering engine. (Surprise, precision@k, NDCG)
Time Series Forecasting
June 8, 2026 – Present
Multi-horizon economic indicator forecasting; benchmarked ARIMA, SARIMA, Prophet and ML approaches. (statsmodels, Prophet, scikit-learn)
Credit Scoring
June 8, 2026 – Present
Risk scoring system validated with Gini, KS statistic, and ROC-AUC. (Logit, XGBoost, SQL)
Revenue Prediction
June 8, 2026 – Present
Benchmarked regression algorithms for company revenue forecasting. (XGBoost, MAE, RMSE, R2)
Churn Prediction
June 8, 2026 – Present
Binary classification to identify at-risk customers with SHAP explainability and retention recommendations. (XGBoost, SHAP, scikit-learn)
KPI Dashboard
June 8, 2026 – Present
Real-time order management dashboard with anomaly alerts. (Power BI, Streamlit, MySQL)
Cultural Fit Analysis
The candidate's project diversity, covering recommendation systems, time series forecasting, credit scoring, and churn prediction, indicates a broad interest and adaptability. Their experience in different companies (CMAIS, OMTPME) and academic background from ESI suggest a well-rounded profile suitable for diverse team environments. The listed soft skills align well with a collaborative and dynamic work culture.
Soft Skills & Operational Fit
The candidate demonstrates strong soft skills including autonomy, structured problem-solving, clear communication, and a continuous learning mindset, which are crucial for a senior data science role. Their experience in Agile environments suggests good operational fit for collaborative development.