Data Science Specialist - Credit

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.

·       Lead and manage data science projects from ideation to deployment, ensuring timely delivery of high-quality solutions.

·       Define project objectives, scope, and deliverables in collaboration with stakeholders.

·       Gather, cleanse, and preprocess large and complex datasets from various internal and external sources, ensuring data quality and integrity.

·       Conduct in-depth exploratory data analysis to understand data distributions, identify patterns, and uncover insights using advanced statistical methods and data visualization tools.

·       Perform advanced statistical analyses to summarize and interpret data, including hypothesis testing, regression analysis, and other inferential techniques.

·       Use domain knowledge to contextualize data analysis findings, identify relevant variables, and tailor solutions to meet business needs.

·       Create, select, and transform features from raw data to improve model performance, leveraging domain knowledge and advanced feature engineering techniques.

·       Develop, optimize, and implement advanced machine learning models for predictive analytics, classification, regression, clustering, and other purposes.

·       Experiment with various algorithms and techniques to identify the best solutions for specific problems.

·       Rigorously evaluate models using techniques such as cross-validation and various performance metrics to ensure models are robust, accurate, and generalizable.

·       Ensure models meet business requirements and industry best practices.

·       Deploy models into production environments, monitor their performance, and make necessary adjustments to maintain and improve their effectiveness over time.

·       Implement automated monitoring and maintenance processes for deployed models.

·       Mentor and provide technical guidance to junior and mid-level data scientists, fostering a culture of continuous learning and development. Share best practices, provide feedback on projects, and support their professional development and growth.

·       Work collaboratively with other data scientists within the team and participate in cross-functional teams, including data engineers, data analysts, business analysts, and domain experts. Aim to create data products and integrate them into the organization's business processes.

·       Prepare detailed documentation of data analysis processes, methodologies, and results.

·       Document data analysis processes, methodologies, and results in detail. Create comprehensive reports and presentations to communicate findings and recommendations to technical and non-technical stakeholders.

·       Keep abreast of emerging trends, methodologies, and tools in data science, machine learning, and artificial intelligence. Participate in continuous learning and apply new knowledge to improve analytical capabilities and drive innovation within the organization.

·       Participate in continuous learning and apply new knowledge to improve analytical capabilities and drive innovation within the organization.

·       Uphold ethical considerations and best practices in data science, including data privacy, confidentiality, and bias mitigation. Ensure that all data practices comply with relevant regulations and standards.

·       Drive strategic decision-making in the financial sector by enhancing credit-related decision-making processes.

·       Identify business opportunities and optimize the overall performance of financial products and services.

·       Apply machine learning techniques and statistical algorithms to develop predictive models for credit-related issues, including risk assessment and fraud detection.

·       Contribute to the team’s knowledge by sharing insights on international methodologies and staying updated on best practices in financial services.

·       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.