Senior Analyst - Data Strategy
Experience Highlights
Mehrnoush Rajabi is a data scientist with a strong foundation in statistical modeling, predictive analytics, and data-driven decision-making across sectors including logistics, healthcare, and sales. At Hello Food, she built regression and classification models that improved delivery logistics and optimized customer experience, translating raw transactional data into actionable insights that shaped operational strategy. Mehrnoush excels at communicating technical findings to non-technical stakeholders, directly influencing process efficiency and strategic planning.
Previously at Abbott, she led cross-departmental reporting efforts by building interactive dashboards in Tableau and automating data workflows using Python and SQL—reducing manual processing time by 40%. Her earlier role as a sales analyst sharpened her ability to align data with business goals, enhancing product targeting, procurement, and campaign outcomes. With a dual academic background in biomedical engineering, Mehrnoush brings a structured, systems-level approach to analytics, and is passionate about connecting data models to measurable business outcomes.
Core Expertise
Mehrnoush specializes in predictive modeling, statistical analysis, and data storytelling. She is proficient in Python (including scikit-learn, Keras, TensorFlow, and PyTorch), SQL, and Excel, with strong visualization skills using Tableau, Power BI, and Matplotlib. Her ability to build robust machine learning models and interpret real-world datasets positions her as a key contributor to AI-driven decision systems. She is also experienced in A/B testing, KPI reporting, and dashboard development, and thrives in cross-functional environments that require clarity, precision, and strategic insight.
Education
Mehrnoush holds both a Bachelor's and a Master's degree in Biomedical Engineering, where she developed her analytical mindset, systems thinking, and quantitative problem-solving skills. Her academic foundation in applied science and research directly informs her technical rigor and domain versatility as a data scientist.
