Data Scientist

About the job

Description We are looking for a skilled Data Scientist with banking domain experience to work on advanced analytics and machine learning solutions. The role involves close collaboration with business and data teams to build scalable models that solve real-world banking and financial problems. Key Responsibilities • Collaborate with senior data scientists, product teams, and banking business stakeholders to understand requirements and define analytical objectives • Perform exploratory data analysis (EDA) to identify trends, patterns, anomalies, and risk indicators in banking datasets • Design, develop, and deploy machine learning models for use cases such as credit risk, fraud detection, customer segmentation, churn prediction, and forecasting • Build and maintain time series and predictive models for financial and transactional data • Evaluate model performance, interpret results, and communicate insights to business stakeholders in a clear, actionable manner • Monitor, retrain, and optimize models to ensure accuracy, compliance, and business relevance • Stay updated with advancements in data science, machine learning, AI, and banking regulations Required Skills & Experience • Strong foundation in machine learning, statistics, and time series forecasting • Proficiency in Python and hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, etc.) • Experience working with banking or financial services data (transactions, loans, cards, risk, compliance, etc.) • Strong SQL skills and experience with large-scale structured and unstructured datasets • Ability to work cross-functionally and explain complex technical concepts to non-technical stakeholders • Experience building scalable and production-ready forecasting and predictive models Qualifications • Bachelors degree in Data Science, Computer Science, Statistics, Mathematics, or a related field • Strong understanding of statistical methods and data analysis techniques • Experience with SQL and/or NoSQL databases • Familiarity with data visualization tools such as Tableau, Power BI, or similar • Knowledge of machine learning algorithms (supervised & unsupervised) • Exposure to cloud platforms such as AWS, Azure, or Google Cloud • Understanding of NLP techniques (preferred for use cases like customer feedback or document analysis) • Strong communication skills and ability to work independently or in a collaborative, fast-paced environment (ref:hirist.tech)

Requirements

• Machine Learning
• Python
• Data Analysis
• SQL
• Statistical Analysis

Qualifications

• Bachelor's Degree

Preferred Technologies

• Machine Learning
• Python
• Data Analysis
• SQL
• Statistical Analysis