Senior Data Scientist

Our client is seeking a Senior Data Scientist to join their Data & Analytics organization and lead the development of advanced machine learning and predictive analytics solutions. This role is ideal for a hands-on data science leader who enjoys solving complex business problems, building production-level ML models, and partnering with cross-functional teams to deliver measurable business impact.

The Senior Data Scientist will play a key role in developing and operationalizing machine learning solutions across areas including risk, fraud, customer analytics, marketing, and portfolio management. The successful candidate will bring strong technical expertise across the full machine learning lifecycle and the ability to translate complex data insights into actionable business recommendations.

Responsibilities

• Lead the design, development, validation, deployment, and monitoring of machine learning and predictive analytics solutions.
• Build, optimize, and maintain production-grade models that drive business outcomes across risk, fraud detection, customer behavior, marketing analytics, and portfolio management.
• Perform exploratory data analysis, feature engineering, statistical analysis, and hypothesis testing to identify trends and actionable insights.
• Develop scalable analytics solutions using large structured and unstructured datasets.
• Translate complex analytical findings into clear recommendations for technical teams, business stakeholders, and leadership.
• Create dashboards, visualizations, and reporting solutions to communicate model performance, insights, and business impact.
• Partner with data engineering teams to define data requirements, improve data pipelines, and ensure data quality.
• Establish and promote best practices around machine learning development, MLOps, model governance, and analytics processes.
• Lead proof-of-concept initiatives evaluating emerging machine learning techniques and AI technologies.
• Own the complete model lifecycle, including feature engineering, training, validation, deployment, monitoring, retraining, and optimization.
• Mentor entry level data scientists through technical guidance, code reviews, and knowledge sharing.
• Provide technical leadership around modeling strategies, architecture decisions, and analytical methodologies.