Strategic Insights Analyst

K Health•New York, NY

10h•$125,000 - $150,000•Hybrid

About The Position

K Health is looking for a Strategic Insights Analyst with an AI-first mindset to drive better decisions, faster. This role sits at the intersection of operations, data science, and AI-powered automation, making it ideal for someone who can combine rigorous analytical thinking applied to business intuition, while leveraging cutting-edge AI tools to generate insights and translate them into action. You'll play a pivotal role in shaping K Health's growth by working cross-functionally with Product, Operations, Marketing, and Finance teams to inform our strategic roadmap. This role requires someone who can think like a top-tier business consultant but execute like a data scientist—leveraging SQL, Python, AI agents, and data-driven decision-making to solve high-impact business problems at scale. This is a high-impact individual contributor role, perfect for someone who thrives in a fast-paced, highly collaborative, and AI-enhanced environment. Unfortunately, this position does not provide immigration support or sponsorship at this time.

Requirements

4-6 years of experience in analytics, strategy, consulting, business operations, or a related field, with demonstrated experience leveraging AI tools for productivity gains.
AI First Mindset—proven track record of identifying and implementing AI-powered solutions to enhance analytical workflows and business processes.
Strong SQL proficiency—ability to write complex queries and manipulate large datasets, with experience using AI tools to enhance SQL workflow efficiency.
Solid Python knowledge (e.g., pandas, NumPy, scikit-learn) with experience using Python in conjunction with AI agents and automation tools.
Experience with AI-powered analysis tools—familiarity with command line AI agents, LLM-based coding assistants, and automated analysis pipelines.
Solid foundation in statistics and data science concepts (e.g., hypothesis testing, regressions, forecasting) with understanding of how AI can augment these techniques.
Highly analytical and metrics-driven, with the ability to define KPIs, size opportunities, and measure business performance using both traditional and AI-enhanced methods.
Strong strategic problem solver—able to break down ambiguous problems and apply a first-principles approach while leveraging AI for faster iteration.
Excellent communication skills—able to translate data into compelling, actionable insights, using AI tools to enhance visualization and storytelling.
Scrappy and resourceful—you find a way to get things done, often by creatively applying AI tools to multiply your impact.
Fast learner—you can quickly grasp new concepts, tools, AI technologies, and business models.

Nice To Haves

Experience with BI tools (e.g., Looker, Tableau, Mode) and their AI-enhanced features
Familiarity with machine learning concepts and MLOps
Experience with measuring clinical outcomes
Experience with prompt engineering and LLM optimization
Exposure to A/B testing methodologies and automated experimentation platforms
Experience building or deploying AI agents for business analysis
Knowledge of AI workflow automation tools (e.g., Langchain, AutoGPT, or similar)

Responsibilities

Apply analytical rigor and AI-powered tools to strategic business problems, uncovering actionable insights that drive key company metrics through both traditional analysis and AI augmentation.
Enable self-serve analytics and empower stakeholders to access data to facilitate data-driven decision making at scale
Partner cross-functionally with Clinical Operations, Marketing, Product and Finance teams to identify opportunities, develop hypotheses, and execute in-depth analyses using both traditional and AI-enhanced methods.
Develop and own key performance metrics, leveraging AI tools to automate monitoring, anomaly detection, and insight generation.
Leverage SQL and Python through AI agents to analyze large datasets more efficiently, build data models, and create self-updating dashboards that help scale decision-making.
Use statistics, data science techniques, and AI tools (e.g., regression analysis, forecasting, clustering, LLM-powered analysis) to drive better predictions and recommendations.
Build and deploy AI-powered analysis workflows using command line agents that can execute complex SQL queries, perform Python-based analysis, and generate insights autonomously.
Communicate insights effectively, using AI tools to help transform complex data into crisp, actionable recommendations for leadership and stakeholders.
Develop scalable operational processes that leverage our data assets in creative ways using data-driven techniques, large language models, automated pipelines, and AI agents.