.**RH**:Raul García**Position**:Data Scientist with Python**Location**:Aguascalientes, Mexico**Industry - Sector**:MALS**What you'll do?**- Analyze large datasets to identify trends, patterns, and relationships that can drive business insights.- Perform exploratory data analysis (EDA) to gain a deeper understanding of datasets and prepare them for modeling.- Develop machine learning models for classification, regression, clustering, and recommendation systems using Python libraries (e.G., Scikit-learn, TensorFlow, Keras).- Fine-tune and optimize models for better performance, accuracy, and scalability.- Clean and preprocess raw data from various sources to ensure quality and readiness for analysis and model development.- Handle missing data, outliers, and feature engineering to enhance model performance.- Create data visualizations using tools like Matplotlib, Seaborn, or Plotly to convey insights clearly to stakeholders.- Present findings and actionable insights to business leaders and non-technical teams in a clear and concise manner.- Work closely with cross-functional teams (engineering, business, marketing) to understand data requirements and deliver solutions.- Collaborate on the development of new data products and features that add value to the business.- Stay current with the latest trends in data science, machine learning, and artificial intelligence.- Explore new tools, techniques, and methodologies to improve analysis and modeling processes.**What you'll bring**:- Strong proficiency in Python, with expertise in libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, Keras, PyTorch, etc.- Experience with scripting and automation for data analysis and model deployment.- Hands-on experience with machine learning algorithms such as linear regression, logistic regression, decision trees, SVMs, k-means clustering, random forests, and gradient boosting.- Knowledge of deep learning techniques, especially for neural networks and natural language processing (NLP).- Solid foundation in statistics, including hypothesis testing, statistical inference, and experimental design.- Experience with A/B testing, time series analysis, and Bayesian statistics.- Expertise in cleaning, transforming, and processing large datasets for analysis and modeling using Python and related tools.- Familiarity with handling missing data, outliers, and feature engineering.- Proficiency in visualizing data insights using Python libraries (e.G., Matplotlib, Seaborn, Plotly, Bokeh).- Ability to create clear, informative visual reports for non-technical stakeholders.- Experience working with large datasets and big data tools (e.G., Apache Spark, Hadoop, SQL).- Familiarity with cloud-based data platforms (e.G., AWS, Google Cloud, Azure) is a plus.- Familiarity with version control systems such as Git.- Experience with collaborative tools like Jupyter Notebooks, GitHub, or GitLab.- Strong knowledge of SQL for querying relational databases