VP, Data Scientist

About the role

About Columbia Threadneedle Investments Working at Columbia Threadneedle Investments you'll find growth and career opportunities across all of our businesses. We're intentionally built to help you succeed. Our reach is expansive with a global team of 2,300 people working together. Our capability is diverse with more than 550 investment professionals sharing global perspectives across all major asset classes and markets. Our clients have access to a broad array of investment strategies, and we have the capability to create bespoke solutions matched to clients' specific requirements. Our unwavering focus on our clients and strong financial foundation connects each of our enterprise businesses - Ameriprise Financial, Columbia Threadneedle Investments and RiverSource Insurance and Annuities. Job Description Where you'll fit in & what our team goals are... This role is a critical enabler of data-driven decision-making across Columbia Threadneedle Investments EMEA, based in London. The position reports to the Vice President of Columbia Analytics and EDAI AI Strategy and is a senior individual contributor role requiring expertise in Asset Management or Financial Services. This role is specifically intended for an experienced analytics professional who has operated within an investment management or broader financial services environment and understands the data, decision-making processes, and regulatory context of the industry. Significant experience partnering with senior stakeholders in Asset Management, Investments, Distribution, Product, Client, and other related functions is essential. The individual acts as a trusted analytics advisor to EMEA business leaders, translating advanced analytics, statistical modeling, and data science outputs into clear, actionable insights that directly support business decisions within an Asset Management context. Success in the role depends on the ability to connect analytical work to real investment, client, and commercial outcomes, and to communicate effectively with non-technical stakeholders who are accountable for those outcomes. Operating within a highly matrixed, global organization, the role requires strong influencing skills and the ability to lead without formal authority. The individual is expected to build durable partnerships across EMEA and global teams, ensuring analytical initiatives are tightly aligned to business priorities, enterprise standards, and the realities of an AM/FS operating environment. This position is designed for a senior practitioner who proactively identifies opportunities where analytics, machine learning, and data capabilities can materially improve decision-making within Asset Management. The role emphasizes problem framing in complex, ambiguous situations, and applying analytical techniques in ways that are practical, relevant, and embedded into existing business processes over time. While the role does not include direct people management, it is supported by a dedicated team of data scientists based in India. The individual provides leadership through clear definition of business problems, prioritization of work aligned to EMEA needs, and close partnership with global analytics resources. Accountability includes end-to-end ownership of analytical initiatives, senior stakeholder engagement, and informal guidance to ensure high-quality, business-relevant outcomes. Overall, this role is suited to a seasoned analytics leader with demonstrated experience in Asset Management or Financial Services, & strong business acumen, and the credibility to operate in a matrix structure. Impact is driven through insight, influence, and domain expertise rather than formal line management. How you'll spend your time... Identify, develop and implement increasingly complex analytical solutions leveraging tools such as predictive modeling, advanced machine learning techniques, simulation, optimization solutions, etc. Engage collaboratively with business leaders and/or analysts to provide analytical thought leadership and support for business problems. Identify and interpret business needs, define high-level business requirements, strategy, technical risks, and scope. Develop, document, and communicate business-driven analytic solutions and capabilities, translating modeling and analytic output into understandable and actionable business knowledge. Manage dataset creation including data extraction, derived and dependent variable creation, and data quality control processes for analytics, model development, and validation. Monitor execution of analytical solutions, including criteria specification, data sourcing, segmentation, analytics, selection, delivery, and back-end data capture results. Contribute to ongoing expansion of data science expertise and credentials by keeping up with industry best practices, developing new skills, and knowledge sharing. Work cross functionally to develop standardized/automated solutions and adopt best practices. P