Manager, Data Science

Role Overview

• 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.

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.
• 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

Tech Stack

• Cloud
• SQL

Benefits

• health, dental, vision, 401k and much more