Manager-Data Science

Job Summary

The Manager-Data Science will play a crucial role in driving data-driven decision-making processes within the organization. With a focus on leveraging machine learning and artificial intelligence the candidate will lead data science projects that enhance business operations. The role requires a strong foundation in Python and offers an opportunity to work in a hybrid model ensuring a balance between work and personal life.

Responsibilities

• Lead data science initiatives to support strategic decision-making and operational efficiency.
• Oversee the development and implementation of machine learning models to solve complex business problems.
• Collaborate with cross-functional teams to integrate AI solutions into existing systems.
• Provide insights and recommendations based on data analysis to improve business processes.
• Ensure the accuracy and reliability of data-driven models and algorithms.
• Develop and maintain scalable data pipelines and infrastructure.
• Monitor and evaluate the performance of data science projects to ensure alignment with business goals.
• Mentor and guide junior data scientists in best practices and advanced techniques.
• Stay updated with the latest trends and advancements in data science and AI.
• Foster a culture of innovation and continuous improvement within the team.
• Communicate findings and insights to stakeholders in a clear and concise manner.
• Utilize Python for data manipulation analysis and visualization tasks.
• Collaborate with investment banking teams to tailor data solutions to industry-specific needs.

Qualifications

• Possess a strong background in machine learning and artificial intelligence with practical experience.
• Demonstrate proficiency in Python programming for data science applications.
• Have experience in investment banking operations and brokerage as a nice-to-have skill.
• Exhibit excellent problem-solving skills and the ability to work in a fast-paced environment.
• Show strong communication skills to effectively convey technical concepts to non-technical stakeholders.
• Display leadership qualities to guide and mentor team members.
• Be adaptable to hybrid work models and manage time efficiently.