Manager of Data Science

CliftonLarsonAllen•Hudson, WI

About The Position

CLA is looking to hire a Manager of Data Science to join our growing Internal IT team. This role constructs complex solutions that integrate data wrangling, visualization, and advanced modeling techniques into a seamless workflow using software development best practices in R, Python, or other scripting languages. They are comfortable working with APIs, web scraping, and SQL/no-SQL databases. They automate business workflows while integrating stochastic/numeric algorithms in the process. This role will develop more autonomy to develop solutions and will lead others, take on administrative tasks, perform support roles, and get involved in new business development.

Requirements

8 years of relevant experience required.
Experience in data analytics, statistics, data science, AI, financial consulting, computer science or related field required.
Experience with APIs, web scraping, SQL/no-SQL databases, and cloud-based data solutions required.
Supervisory experience required.
Bachelor's degree is required.
Combination of relevant experience, education, and training may be accepted in lieu of degree.

Nice To Haves

Degree in Statistics, Computer Science, Economics, Analytics, Data Science (e.g., Informatics, Data Science, Health Data Science), AI, or related field preferred.
Masters in a Data Science/Analytics/AI is a plus.

Responsibilities

Provide day‑to‑day leadership, coaching, development, and performance management.
Mentor and guide analysts, supporting onboarding, skill development, and continuous learning across career stages.
Conduct workload planning, prioritization, and resource allocation to support multiple concurrent initiatives.
Build and sustain a high‑performing team culture rooted in collaboration, quality, accountability, and innovation.
Lead and oversee large scale analytical and AI initiatives, including data acquisition, transformation, modeling, AI system development, automation, and insight generation.
Provide technical oversight and review of analytical approaches, models, and AI systems to ensure sound methodology, reproducibility, and scientific rigor.
Guide the development and application of advanced statistical, machine learning, and AI-driven solutions, including predictive models, computer vision, large language models (LLMs), and agent-based systems.
Lead the design and oversight of AI-enabled systems, including prompt engineering strategies, retrieval-augmented generation (RAG), embeddings, and agentic workflows.
Establish and evolve analytical and AI best practices, documentation standards, and technical frameworks across the team.
Ensure standards for model and AI system validation, monitoring, evaluation, documentation, and responsible AI use are consistently applied.
Partner with engineering, IT, and data platform teams to enable scalable, reliable, and well governed solutions.
Own delivery outcomes for data science and AI workstreams, ensuring solutions meet quality, performance, and business expectations.
Translate business objectives into clear analytical and AI priorities, balancing near term delivery with long term capability building.
Oversee planning, prioritization, and resourcing across projects and teams.
Monitor solution performance, validation results, and model or AI system stability; guide troubleshooting of complex data, model, or AI issues.
Ensure solutions are productionized effectively, with clear ownership, monitoring, and integration into business workflows.
Establish, refine, and enforce standards for documentation, reproducibility, quality assurance, and governance.
Remove obstacles, manage risks, and ensure consistent execution across initiatives.
Serve as a primary point of contact for business and functional leaders on analytics and AI initiatives.
Partner with stakeholders to define business questions, success metrics, analytical frameworks, and delivery expectations.
Coordinate work across teams, offices, and disciplines to ensure alignment of analytical and AI approaches and outcomes.
Communicate progress, risks, and results clearly to both technical and non technical audiences.
Evaluate and recommend adoption of new data sources, technologies, and analytical and AI tools.
Contribute to enterprise level analytics and AI strategy, including identifying high impact use cases and guiding their transition from concept to production.