Data Scientist, Senior

About the Team

Data Services & Engineering Teams at SWIBsupports, implements & develops industry-leading systems and platforms to support SWIB’s diverse and complex set of investment portfolios and strategies. The team at SWIB strives to be a trusted advisor and partner to the business that is valued as a critical contributor to SWIB’s continued growth and success. We effectivelyleveragetechnology to derive the maximum value from it and achieve SWIB’s business goals. We keep technology aligned with SWIB’s future direction and operate SWIB’s technology according to industry standards.  

Position Overview

Essential activities:

• Lead the design, development, validation, and deployment of advanced analytics, AI,and machine learning solutions that enable data-driven investment decision-making.

• Own the technical approach for analytics products end-to-end: problem framing, data requirements, modeling, evaluation, deployment, monitoring, and ongoing iteration.

• Architect and deploy solutions using GitLab (merge requests, CI/CD pipelines, automated testing, release management) and Terraform (infrastructure as code),establishingstrong engineering practices and reproducibility.

• Design, evaluate, and deploy AI-enabled analytical solutions measuring output quality, detecting hallucinations, and ensuring reliability for decision-making.

• Implement data quality,validation, and AI evaluationframeworks;define reliability metrics, testing protocols, andmonitoring controls ensuringoutputs areaccurate, traceable,andexplainable.

• Design and develop analyticsapplications and internal tools, includinglightweightfront-end interfaces(Power BI,Streamlit,React,orsimilar tools) to communicate findings and drive adoption;apply UI/UX principles ensuring usability, clarity, and intuitive workflows;craft clear narratives about assumptions, limitations, and implications.

• Deploy analytics solutions in cloud environments (Azure or AWS), partnering with engineering/security to ensure secure, scalable, cost-aware deployments.

• Utilize data warehousing technologies (e.g., Snowflake) to support analytics initiatives; collaborate on data modeling and performant query patterns.

• Communicate complex concepts clearly to technical and non-technical stakeholders; translate investment needs into analyticalroadmapsand measurable outcomes.

• Serve as a liaison across investment teams and partner functions (IT, Operations, Legal, HR, Strategic Planning, etc.) to support change management and adoption of analytics solutions.

• Act as a senior team contributor: provide design input, conduct code and analysis reviews, share patterns and best practices, and coach junior staff through pairing, feedback, and knowledge sharing.

The ideal candidate:

• Bachelor’s degree; advanced degree preferred in finance, business, engineering, computer science, computational economics, math, data science, or related discipline.

• Experience in investment management, quantitative finance, and technology; progress toward or completion of the CFA designation is preferred.

• 5+ years of experience in data science, analytics, quantitative research, or similar roles.

• 2+ years of experiencedesigningand deployingAI-enabled analyticalsolutions measuring output quality, detectinghallucinations, and ensuringreliability for decision-making.

• Strongproficiencyin Python and SQL for advanced analytics, data engineering, and model development in production contexts.

• Proven experience deploying and operating production code using GitLab, including CI/CD, merge request workflows, automated testing, and release management.

• Experience using Terraform to provision and manage cloud infrastructure as code.

• Experience building and deploying ML models using modern techniques (regression, classification, clustering, time series/forecasting) with strong evaluation practices and sound statistical reasoning.

• Experience implementing data quality frameworks, validation controls, and reliability metrics/processes for analytical outputs and reports.

• Strong experience with cloud platforms (Azure or AWS) for data storage/processing and deploying analytics solutions; familiarity with security and operational considerations.

• Experience with data warehousing platforms (e.g., Snowflake) to support scalable analytics initiatives.

• Excellent communication skills with the ability to influence decisions through clear storytelling and stakeholder partnership.

• Demonstrated ability to collaborate effectively, coach junior staff, and elevate team standards through reviews, reusable patterns, and documentation.

• Strong workethic, attention to detail, and commitment to disciplined delivery (documentation, Jira ticketing, and best practices).