What are the responsibilities and job description for the Data Scientist position at The Value Maximizer?
About The Role
We are seeking a highly skilled Data Scientist with expertise in Python, R, and PySpark to join our analytics team for a leading banking client. The ideal candidate will leverage advanced analytics, machine learning, and big data technologies to design data-driven solutions, generate actionable insights, and support strategic decision-making in areas such as risk management, fraud detection, customer segmentation, and credit scoring.Key Responsibilities
Work with large-scale structured and unstructured datasets to design, develop, and deploy predictive and prescriptive models.Apply statistical modeling, machine learning, and data mining techniques to address complex business problems in banking.Develop and optimize data pipelines and ETL processes using PySpark for efficient big data processing.Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing using Python and R.Partner with business stakeholders to translate requirements into analytical solutions.Create visualizations and dashboards to communicate insights to technical and non-technical audiences.Ensure compliance with data governance, privacy, and security standards in all analytics work.Required Skills & Qualifications
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or related field. 3-7 years of experience as a Data Scientist, ideally within the banking or financial services domain.Strong programming skills in Python and R, including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, ggplot2, and caret.Hands-on experience with PySpark for distributed data processing and analysis.Strong statistical and mathematical foundation in regression, classification, clustering, time-series analysis, and hypothesis testing.Experience working with large datasets, big data platforms (Hadoop, Spark), and SQL/NoSQL databases.Familiarity with cloud platforms (AWS, Azure, or GCP) and version control (Git).Excellent problem-solving, communication, and stakeholder management skills.Good to have Experience in risk analytics, fraud detection, credit risk modeling, or customer analytics in banking/financial services.
We are seeking a highly skilled Data Scientist with expertise in Python, R, and PySpark to join our analytics team for a leading banking client. The ideal candidate will leverage advanced analytics, machine learning, and big data technologies to design data-driven solutions, generate actionable insights, and support strategic decision-making in areas such as risk management, fraud detection, customer segmentation, and credit scoring.Key Responsibilities
Work with large-scale structured and unstructured datasets to design, develop, and deploy predictive and prescriptive models.Apply statistical modeling, machine learning, and data mining techniques to address complex business problems in banking.Develop and optimize data pipelines and ETL processes using PySpark for efficient big data processing.Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing using Python and R.Partner with business stakeholders to translate requirements into analytical solutions.Create visualizations and dashboards to communicate insights to technical and non-technical audiences.Ensure compliance with data governance, privacy, and security standards in all analytics work.Required Skills & Qualifications
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or related field. 3-7 years of experience as a Data Scientist, ideally within the banking or financial services domain.Strong programming skills in Python and R, including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, ggplot2, and caret.Hands-on experience with PySpark for distributed data processing and analysis.Strong statistical and mathematical foundation in regression, classification, clustering, time-series analysis, and hypothesis testing.Experience working with large datasets, big data platforms (Hadoop, Spark), and SQL/NoSQL databases.Familiarity with cloud platforms (AWS, Azure, or GCP) and version control (Git).Excellent problem-solving, communication, and stakeholder management skills.Good to have Experience in risk analytics, fraud detection, credit risk modeling, or customer analytics in banking/financial services.