h3 Technologies
Position Title Data Scientist
Job Location Atlanta, GA, USA
Hybrid
Must have Skills/Attributes AI Models, Data Scientist, Design, Python, SAS, SQL
Position Description Required Education: • Bachelor's degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field Preferred Education: • Master's degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field Required Experience, Knowledge, and Skills: • Familiarity with graph analytics or network-based fraud detection tools • Knowledge of regulatory frameworks and compliance issues related to fraud and financial crime • Strong communication skills with the ability to explain technical solutions to non-technical stakeholders • Professional experience in data science (10 Years) • Proficient in Python, SQL, SAS, and machine learning techniques (5 Years) • Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure) (5 Years) • Understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis • Experience with visualization tools like Tableau and Power BI (5 Years) • Experience in responsible use of AI if used in solution design (5 Years) • Strong analytical skills and the ability to identify patterns and trends from data Key Responsibilities: • Collect, clean, and analyze large, complex datasets from multiple sources • Develop predictive models and machine learning algorithms to support decision-making and improve business performance • Translate business problems into data-driven solutions with measurable impact • Develop and deploy machine learning models to detect, predict, and prevent fraudulent transactions and behavior patterns • Analyze large volumes of structured and unstructured data from multiple sources to identify fraud trends and root causes • Collaborate with fraud operations, engineering, and compliance teams to implement real-time fraud detection solutions • Design and monitor KPIs to evaluate model performance and improve fraud detection systems over time • Conduct deep-dive investigations into fraud cases, creating detailed reports and actionable insights • Stay current with emerging fraud techniques, industry best practices, and data science tools
Position Description Required Education: • Bachelor's degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field Preferred Education: • Master's degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field Required Experience, Knowledge, and Skills: • Familiarity with graph analytics or network-based fraud detection tools • Knowledge of regulatory frameworks and compliance issues related to fraud and financial crime • Strong communication skills with the ability to explain technical solutions to non-technical stakeholders • Professional experience in data science (10 Years) • Proficient in Python, SQL, SAS, and machine learning techniques (5 Years) • Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure) (5 Years) • Understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis • Experience with visualization tools like Tableau and Power BI (5 Years) • Experience in responsible use of AI if used in solution design (5 Years) • Strong analytical skills and the ability to identify patterns and trends from data Key Responsibilities: • Collect, clean, and analyze large, complex datasets from multiple sources • Develop predictive models and machine learning algorithms to support decision-making and improve business performance • Translate business problems into data-driven solutions with measurable impact • Develop and deploy machine learning models to detect, predict, and prevent fraudulent transactions and behavior patterns • Analyze large volumes of structured and unstructured data from multiple sources to identify fraud trends and root causes • Collaborate with fraud operations, engineering, and compliance teams to implement real-time fraud detection solutions • Design and monitor KPIs to evaluate model performance and improve fraud detection systems over time • Conduct deep-dive investigations into fraud cases, creating detailed reports and actionable insights • Stay current with emerging fraud techniques, industry best practices, and data science tools