The role is responsible for designing, developing, and implementing advanced statistical and machine learning models, analyzing large datasets, and providing actionable insights to support business decisions. The incumbent will work closely with business and technology teams to drive process improvements and ensure robust data solutions.
Key Responsibilities:
- Develop and implement advanced statistical models and machine learning algorithms to analyze complex datasets.
- Build and maintain scalable data pipelines and data processing frameworks using big data technologies.
- Conduct data exploration, feature engineering, and model validation to ensure high-quality analytical outputs.
- Present insights and recommendations through structured reports, dashboards, and visualizations for management and stakeholders.
- Identify opportunities to optimize business processes using data-driven solutions and advanced analytics techniques.
- Stay updated with emerging trends and advancements in data science, machine learning, and data engineering to enhance capabilities.
Job Qualifications:
- Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field; Master's degree preferred.
- 510 years of experience in technology or analytics roles, with 35 years in data engineering, data science, or data analysis, ideally within financial services.
- Strong proficiency in Python and/or Spark for complex data engineering and analytics tasks.
- Hands-on experience with machine learning libraries such as scikit-learn and TensorFlow.
- Knowledge of data engineering tools and big data platforms, including Hive, Impala, Oozie, and Hadoop.
- Solid understanding of statistical modeling, predictive analytics, and machine learning techniques.
