Head of Data Science and Engineering - Cornwallis Elt : Job Details

This range is provided by Cornwallis Elt. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$250,000.00/yr - $300,000.00/yr

A global Asset Manager is looking for a Head of Data Science & Engineering that will develop data solutions to address firm and industry opportunities. Responsibilities include designing and implementing data pipelines, data lakes, analytical dashboards, predictive models, machine learning algorithms, and data science applications using various data technologies and platforms.

A brand-new role to propel forward the business whilst seeing data as an integral part, needing someone to focus on the engineering and management of data before diving into the Data Science/AI world.

Key Responsibilities

• Strategic Alignment: Partner with leaders from Technology, Transformation, Investment, Portfolio Management, and Investor Relations teams to refine and execute the firm's data science strategy.
• Research and Application: Research and apply best practices and methodologies in data science for private markets investment firms, including machine learning, artificial intelligence, or deep learning.
• Industry Awareness: Stay updated on private markets industry trends, regulatory needs, software offerings, and investment team feedback to guide product decisions and maintain a competitive edge.
• Data Science Use Cases: Develop market-leading use cases for data science applications and predictive models to support investment and risk professionals.
• Data Strategy and Governance: Align data strategy with firm priorities, establish data sources, identify necessary tools, and oversee data governance and quality standards.
• Data Platform Enhancement: Enhance the firm's data platform, including data lakes, data pipelines, and reporting infrastructure.
• Product Development: Collaborate with the Transformation Office to deliver and enhance the firm's data and analytics products, ensuring alignment with strategic transformation initiatives.
• User-Centric Approach: Champion a user-centric approach in predictive modeling, data science applications, and content creation, focusing on usability and overall user satisfaction.
• Performance Metrics: Establish KPIs to measure the outcomes and impacts of data initiatives. Monitor and validate existing solutions to improve them and deliver outcome-oriented impact analysis to stakeholders and leadership.
• Team Management: Manage a data team that may include data analysts, data engineers, data scientists, and machine learning engineers. Provide leadership, mentorship, and professional development opportunities.
• Innovation and Growth: Foster a culture of innovation and continuous improvement within the team, mentoring emerging innovators and enhancing team operating models and practices.

Knowledge and Experience Required

• Education: Bachelor's degree in Engineering, Technology, Data Science, or a related field. An advanced degree is preferred.
• Expertise: Significant experience in data engineering, data science, machine learning, and natural language processing within investment firms.
• Industry Knowledge: Track record of working with and organizing data within financial services, asset management, or private markets.
• Technical Skills: Proficiency with data lake concepts and tools such as Azure Databricks or Snowflake, and expertise in metadata management and data governance best practices.
• Programming: Proficient in programming languages such as Python, R, or SQL, and frameworks like TensorFlow, PyTorch, Keras, HuggingFace, or Spark. Experience with analytics tools like Azure Synapse, and reporting platforms like PowerBI or Tableau.
• Data Management: Proficiency in acquiring, storing, processing, and delivering structured and unstructured data for analytical applications.
• Governance and Quality: Expertise in data governance, data quality management, and the implementation of analytics platforms.
• Team Leadership: Strong experience managing and developing high-performing teams, inspiring cross-functional collaboration, and leading globally distributed teams.
• Analytical Skills: Strong analytical and strategic thinking, with the ability to translate data into actionable business insights.
• Problem-Solving: Excellent problem-solving skills, attention to detail, and the ability to navigate complex data processes and organizational challenges.
• Communication Skills: Exceptional communication and interpersonal skills, with the ability to engage and influence stakeholders at all levels of the organization.
• Cross-Functional Leadership: Experience in leading cross-functional initiatives with strong project management abilities and organizational skills.

Director

Full-time

Information Technology