Team Lead Data Scientist

Description:

Job Overview:

We are seeking a highly skilled and motivated Team Lead Data Scientist to join our team. The ideal candidate will be responsible to utilize data, machine learning, statistical and mathematical models to solve business problems and enabling the effective use of data by utilizing cutting-edge technologies.

Roles and Responsibilities:

• Provide strategic direction for data science initiatives that align with organizational goals, develop and execute a data science strategy that supports the organization’s long-term vision. Identify high-impact projects and prioritize them based on business value and feasibility. Align data science goals with business objectives and ensure resources are effectively allocated.
• Build and nurture a high-performing data science team. Recruit, mentor, and retain top talent in data science and analytics, foster a collaborative and innovative team culture. Implement training programs and career development plans for team members.
• Drive cross-functional collaboration and influence data-driven decision-making across the organization. Act as a bridge between data science and other business units, fostering collaboration and alignment, promote data literacy and advocate for data-driven decision-making. Present findings and recommendations to senior leadership and cross-functional teams.
• Ensure robust governance and risk management practices for all data science models and solutions, establish model governance frameworks, including versioning, validation, and monitoring processes. Implement risk management strategies to mitigate model, data risks and ensure compliance with ethical standards and regulatory requirements.
• Drive innovation and research to advance the organization’s data science capabilities, Lead research initiatives to explore new methodologies, technologies, and data sources. Encourage experimentation and prototyping to test new ideas and approaches and collaborate with academic and industry partners to stay at the forefront of data science innovation.
• Successfully implement and scale the organization’s data and AI strategy, develop and oversee the implementation of the organization's data and AI strategy. Ensure that data and AI initiatives are aligned with the broader business strategy and monitor the scalability and sustainability of data and AI solution.
• Manage customer centricity within area of responsibility.
• Ensure relevant and up-to-date performance management processes are implemented.

Minimum Requirements and Qualifications:

• At least 10+ years’ experience as a data analyst with SAS and or Python experience.
• Knowledge of the regulatory analytics domain within banking/insurance sector will be an advantage.
• Firm grip on analytics tools like SQL, R and Python.
• Working knowledge or experience in the fields of data management, operations analytics, big data and artificial intelligence is preferred.
• Project management
• Conflict management
• Stakeholder management
• Writing and Reporting
• Applying Expertise and Knowledge
• Business and Financial acumen
• Data Architecture, Data Modelling and Data Pipelining
• Solutions Architecture
• Model Testing skills
• Interpersonal skills
• Persuading and Influencing skills
• Professional and technical proficiency
• Analysis and Judgment

Requirements:

• Provide strategic direction for data science initiatives that align with organizational goals, develop and execute a data science strategy that supports the organization’s long-term vision. Identify high-impact projects and prioritize them based on business value and feasibility. Align data science goals with business objectives and ensure resources are effectively allocated.
• Build and nurture a high-performing data science team. Recruit, mentor, and retain top talent in data science and analytics, foster a collaborative and innovative team culture. Implement training programs and career development plans for team members.
• Drive cross-functional collaboration and influence data-driven decision-making across the organization. Act as a bridge between data science and other business units, fostering collaboration and alignment, promote data literacy and advocate for data-driven decision-making. Present findings and recommendations to senior leadership and cross-functional teams.
• Ensure robust governance and risk management practices for all data science models and solutions, establish model governance frameworks, including versioning, validation, and monitoring processes. Implement risk management strategies to mitigate model, data risks and ensure compliance with ethical standards and regulatory requirements.
• Drive innovation and research to advance the organization’s data science capabilities, Lead research initiatives to explore new methodologies, technologies, and data sources. Encourage experimentation and prototyping to test new ideas and approaches and collaborate with academic and industry partners to stay at the forefront of data science innovation.
• Successfully implement and scale the organization’s data and AI strategy, develop and oversee the implementation of the organization's data and AI strategy. Ensure that data and AI initiatives are aligned with the broader business strategy and monitor the scalability and sustainability of data and AI solution.
• Manage customer centricity within area of responsibility.
• Ensure relevant and up-to-date performance management processes are implemented.

• At least 10+ years’ experience as a data analyst with SAS and or Python experience.
• Knowledge of the regulatory analytics domain within banking/insurance sector will be an advantage.
• Firm grip on analytics tools like SQL, R and Python.
• Working knowledge or experience in the fields of data management, operations analytics, big data and artificial intelligence is preferred.
• Project management
• Conflict management
• Stakeholder management
• Writing and Reporting
• Applying Expertise and Knowledge
• Business and Financial acumen
• Data Architecture, Data Modelling and Data Pipelining
• Solutions Architecture
• Model Testing skills
• Interpersonal skills
• Persuading and Influencing skills
• Professional and technical proficiency
• Analysis and Judgment