Senior Consultant, Data Science and Analytics

Sr Consultant, Data Science and Analytics

The Data Science Development team is responsible for building and supporting Trans Union's proprietary analytics platforms and scientific computing capabilities that power advanced solutions across Insurance Analytics and other business domains. The team partners closely with Global Data Science & Analytics, Insurance Analytics, and Global Technology to deliver scalable machine learning, mathematical programming, and big data solutions that drive innovation across the organization.

This role reports through the Data Science Development organization within Global Data Science & Analytics (DSA). This is a remote position which may require occasional in-person attendance at work-related events at the discretion of management.

Role Overview And Core Responsibilities

• Drive the development, enhancement, and maintenance of Trans Union's proprietary Insurance Analytics platform (InsureR), delivering scalable and high-quality machine learning and analytic solutions.
• Design, develop, and optimize advanced analytic applications using C++, Python, R, SQL, Hive, Spark, and related technologies.
• Improve analytic platform usability by contributing to front-end solutions and user experience enhancements that increase adoption and efficiency.
• Provide hands-on support for regional analytic environments, troubleshooting technical issues and ensuring platform stability and performance.
• Collaborate with Global Technology teams to maintain and enhance high-performance computing (HPC) infrastructure and associated analytic frameworks.
• Partner with business stakeholders and regional teams to identify opportunities for adopting analytic products, services, and data-driven strategies.
• Contribute to research and innovation initiatives focused on machine learning, scientific computing, and advanced analytics methodologies.
• Serve as a subject matter expert on analytic development, machine learning, and scientific computing for enterprise-wide projects.
• Mentor junior team members, support knowledge sharing initiatives, and help cultivate a high-performance, collaborative culture.
• Participate in talent acquisition activities, including interviewing and evaluating prospective candidates.

Required Knowledge And Experiences

• Master's degree in Statistics, Economics, Applied Mathematics, Financial Mathematics, Computer Science, Engineering, Operations Research, or another quantitative discipline with 3+ years of relevant experience; or Bachelor's degree with 5+ years of relevant professional experience.
• Experience developing advanced analytics, machine learning, or mathematical programming solutions within a production environment.
• Strong understanding of numerical methods, optimization techniques, scientific computing, and machine learning concepts used to solve complex business problems.
• Demonstrated ability to manage multiple projects simultaneously while collaborating effectively across cross-functional teams in a fast-paced environment.
• Experience working within industries such as financial services, insurance, fraud, risk, or digital marketing is highly valued.
• Strong business acumen with the ability to translate technical findings into actionable insights for technical and non-technical stakeholders.
• Excellent written and verbal communication skills in English and Spanish.
• Ability to travel up to 10-20% as business needs require.

• Advanced programming experience in C++, preferably supporting scientific computing or analytical applications.
• Strong proficiency in Python and/or R for machine learning, statistical modeling, and data science development.
• Advanced SQL skills and experience working with large-scale data environments including Hadoop, Spark, and Hive.
• Experience integrating machine learning frameworks such as XGBoost, LightGBM, or H2O into enterprise analytic solutions.
• Experience supporting or developing applications within High Performance Computing (HPC) environments.
• Familiarity with big data architecture, distributed computing concepts, and modern data engineering practices.

We're also looking for the preferred skills below. Whether you are proficient or could use some brushing up, we're happy to support your career development and growth in:

• Experience developing data science applications with front-end technologies such as Shiny, Streamlit, Dash, or Tableau.
• Knowledge of Apache Arrow and modern data interchange frameworks.
• Familiarity with ETL methodologies, data integration frameworks, and…