Data Scientist Senior - Credit

Objective of the Rol:

• Plays a crucial role in leveraging advanced data science techniques to enhance credit-related decision-making processes. With 4 to 7 years of experience in data science, statistics, applied mathematics, actuarial science, or similar fields, play a key role in building models that predict credit risk, detect fraud, and find growth opportunities. Their expertise is critical in driving data-driven strategies that align with the organization’s financial goals. 
• Responsibilities typically include leading data collection and preprocessing efforts, conducting comprehensive exploratory data analysis, performing sophisticated statistical analyses, advanced feature engineering, model evaluation, and validation, deploying and monitoring models in production environments, and mentoring junior and mid-level data scientists. Expected to have deep expertise in machine learning algorithms, statistical techniques, and domain knowledge relevant to the organization. 
• They lead projects aimed at improving organizational efficiency, identifying growth opportunities, and minimizing risks using machine learning. Overall, the goal is to utilize advanced data science methodologies and tools to solve complex problems, generate valuable insights, improve decision-making processes, and drive innovation, thereby contributing to the organization's success and growth. 

Required Knowledge and Experience:

• 4-7 years in data science, statistics, programming, or related fields, with extensive hands-on experience in data analysis, machine learning, and statistical modeling, with at least 2 years in roles specifically related to credit or finance. 

• Bachelor’s or Master’s degree in Data Science, Statistics, Applied Mathematics, Actuarial Science, Computer Science, or a related discipline. Desirable: Advanced coursework or specialized training in machine learning, statistics, or data science.
• Experience leading and executing data science projects in diverse domains and working on a wide range of projects, from exploratory analysis to building and deploying advanced machine learning models and predictive analytics solutions.
• Proficiency in programming languages commonly used in data science, such as Python or R. Familiarity with libraries and frameworks like NumPy, Pandas, scikit-learn (Python), or tidyverse (R) for data manipulation, analysis, and modeling.
• Experience in advanced statistical techniques, including multivariate analysis, time series analysis, hypothesis testing, and experimental design. Ability to apply statistical methods to analyze complex datasets and derive meaningful insights.
• Extensive experience in developing, fine-tuning, and deploying machine learning models and predictive analytics solutions.
• Deep understanding of a wide range of machine learning algorithms, including supervised learning (e.g., linear regression, logistic regression, decision trees, random forests), unsupervised learning (e.g., clustering, dimensionality reduction), and ensemble methods (e.g., gradient boosting, bagging). Ability to select and apply appropriate algorithms to solve specific business problems. 

• Knowledge of deep learning techniques and frameworks, such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning. Experience with deep learning libraries like TensorFlow, Keras, or PyTorch. 
• Proficiency in SQL for querying, manipulating, and analyzing data stored in relational databases. Familiarity with NoSQL databases and other data storage solutions. 
• Familiarity with big data technologies and frameworks, such as Apache Hadoop, Spark, and distributed computing systems. Ability to work with large-scale datasets and optimize algorithms for parallel processing and scalability. 
• Strong analytical and problem-solving skills, with the ability to critically evaluate data and models, identify biases, and make data-driven decisions. 
• Advanced skills in data visualization and storytelling, using tools like Matplotlib, Seaborn, ggplot2, Plotly, or Tableau. Ability to create clear and compelling visualizations to communicate complex findings to non-technical stakeholders. 
• Substancial understanding of the industry in which the organization operates. Knowledge of industry-specific trends, regulations, and business processes. Ability to apply domain knowledge to contextualize data analysis findings and drive strategic decision-making. 
• Understand and apply ethical considerations and best practices in data science, including data privacy, confidentiality, and bias mitigation. Ability to ensure ethical and responsible use of data in all data science activities. 

Digital FEMSA está comprometida con un lugar de trabajo diverso e inclusivo. 
Somos un empleador que ofrece igualdad de oportunidades y no discrimina por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad u otra condición legalmente protegida. 
Si desea solicitar una adaptación, notifique a su Reclutador.

Last updated on Aug 21, 2024