•Own end-to-end analytics for key business functions by defining metrics, building dashboards, uncovering insights, and recommending actions that improve growth, retention and operational efficiency.
•Partner closely with Product, Engineering, Marketing, Operations and Leadership to translate business problems into analytical solutions, experiments and decision-making frameworks.
•Build, maintain and improve robust data models, reporting layers, and data pipelines to enable accurate, scalable and self-serve analytics across teams.
•Apply advanced analytical techniques such as forecasting, segmentation, A/B testing, statistical analysis and machine learning to solve high-impact business problems.
•Present findings clearly to senior stakeholders through structured storytelling, executive-ready presentations and actionable recommendations.
•Analyze product, customer, marketing and operational data to identify patterns, opportunities, risks, and areas for process improvement.
•Work with engineering and product teams to define event tracking, data schemas, and reporting standards for reliable decision-making.
•Ensure data quality, consistency and integrity through validation, monitoring and documentation practices.
•Support experimentation and performance measurement by designing success metrics and evaluating results with statistical rigor.
•Create dashboards and reports using BI tools that help business teams monitor KPIs and act quickly on insights.
Required Qualifications
•Bachelor’s degree in Data Analytics, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related field.
•2–5 years of experience in data analytics, business intelligence, or a similar role, preferably in a start up, digital health, or HealthTech environment.
•Strong command of SQL and solid working knowledge of Python or R for analysis, automation, and modelling.
•Experience with BI and visualization tools such as Tableau, Power BI, Looker, Sigma, or similar platforms.
•Strong understanding of experimentation, hypothesis testing, segmentation, forecasting, and core statistical methods.
•Experience working with large, messy, multi-source datasets and turning them into clear recommendations.
•Excellent communication skills, with the ability to explain complex findings to non-technical and senior stakeholders.