Data Scientist - Consultant

Description

Job Summary: We are seeking a Mid-Level Data Scientist to develop end-to-end solutions, analyze data, and translate complex results into business impact, with a focus on forecasting, NLP, and transformer-based models. Key Highlights: 1. Challenging projects with real impact 2. Modern environment (cloud, MLOps, LLMs) 3. Continuous professional growth Within Eraneos’ international Data practice, we are looking for a **Mid-Level Data Scientist** with a strong technical foundation in data analysis, machine learning, and applied statistics, capable of developing end-to-end solutions and translating complex results into business impact. You will join a multidisciplinary team working on real-world use cases—from data exploration to model production—focusing especially on **forecasting, NLP, and transformer-based models**. ### **Responsibilities** * Analyze, clean, and transform large volumes of structured and unstructured data * Develop **machine learning** and **deep learning** models for business problems * Implement **forecasting and time series** solutions * Design and analyze experiments (**A/B testing**) * Build reproducible data and model pipelines * Explain results and models to non-technical stakeholders * Collaborate with Data Engineering, BI, and Product teams * Participate in the full model lifecycle (**MLOps**) ### **What We Offer** * Challenging projects with real impact * Modern environment (cloud, MLOps, LLMs) * High-caliber technical team * Continuous professional growth * Official certification program * Flexible working hours and remote work * Professional experience: **2–5 years** in Data Science or related roles * Ability to work autonomously on complex projects * Practical, business-impact-oriented mindset **Technology Stack** * Python (advanced), + PostgreSQL * SQL * Databricks * Pandas, NumPy * PySpark / Spark * Matplotlib, Seaborn or Plotly * Experimental design / A/B testing * Scikit-learn * Regression and classification models * Clustering (K-Means, DBSCAN…) * Feature engineering and variable selection * Gradient boosting (XGBoost, LightGBM, CatBoost) * Model validation and metrics (AUC, F1, RMSE…) * ARIMA / SARIMA * Prophet * Time-series-aware models (LightGBM) * Fundamentals of neural networks * PyTorch or TensorFlow/Keras * CNNs and RNNs/LSTMs (conceptual-practical level) * Text preprocessing (TF-IDF, embeddings) * Hugging Face Transformers * BERT / RoBERTa-type models * Model fine-tuning * Prompt engineering and usage of LLM APIs * SHAP, LIME * Feature importance * MLflow * Git (GitHub/GitLab) * CI/CD (conceptual) * Modular and testable code (Pytest/unittest)**Highly Valuable** * R, Scala * Polars, Dask * Power BI / Tableau / Streamlit * Bayesian statistics or survival analysis * Recommendation systems or anomaly detection * Advanced deep learning (LSTM, TFT, transfer learning) * RAG, LangChain, LlamaIndex * Vector databases (FAISS, Pinecone, etc.) * Optuna / AutoML * AWS, GCP, Azure, Snowflake * Airflow, Docker, Kubernetes * Model APIs (FastAPI, Flask) * Web scraping and API consumption * Causal inference techniques * Cloud or ML certifications *At ***Eraneos***, we are committed to respecting diversity and equal opportunity. We firmly believe that a diverse workforce is key to success. Therefore, we foster an inclusive workplace, without discrimination based on age, sexual orientation, nationality, religion, marital status, disability, or gender identity.