I am ✨ Meenu ✨, a Data Scientist with a multidisciplinary edge — combining data science, healthcare, and economics to turn complex data into decisions that matter.

I hold two master's degrees — a master's in Epidemiology & Biomedical Data Science from the University of Oulu, Finland, and a master's in Public Health with a specialization in Health Economics from Umeå University, Sweden. This multidisciplinary background allows me to combine technical data skills with statistical, healthcare, and economic perspectives when solving real-world problems.

I work across Python, SQL, R, Power BI, Snowflake, BigQuery, and Excel, taking data from querying and transformation to statistical analysis, visualization, KPI monitoring, and actionable recommendations.

I have worked with 50,000+ healthcare records and 12,000+ business records, building analytical workflows, dashboards, and reporting solutions that turn raw data into meaningful operational and business insights. My work has included improving reporting accuracy by 25% and reducing manual reporting effort by 40%, while applying data quality checks, KPI analysis, and statistical methods to make results more reliable and decision-ready.

What drives me is the story behind the data — uncovering patterns, connecting them to the context behind the numbers, and translating complex analysis into clear narratives that reveal what matters, why it matters, and where action can create impact. To me, great data storytelling is where evidence meets context and insight becomes action — enabling decision-makers to move from simply understanding the numbers to acting on them with confidence.

I am particularly interested in building data-driven solutions at the intersection of healthcare, business, and emerging AI — where rigorous analysis can translate into smarter decisions and measurable impact.

• Data Science & Analytics: Turning complex datasets into actionable insights and data-driven recommendations.
• Machine Learning & AI: Building predictive models and exploring AI-driven solutions for real-world problems.
• Healthcare & Biomedical Analytics: Applying data science and statistical thinking to healthcare and public health challenges.
• Business Intelligence: Building dashboards and KPI reporting that help teams understand performance and make better decisions.
• Statistical Modeling: Exploring patterns, relationships, uncertainty, and real-world outcomes through statistical analysis.
• Data Visualization & Storytelling: Creating clear and impactful visual stories using Power BI, Tableau, and other visualization tools.
• Generative AI: Exploring how GenAI and modern AI tools can improve analytics, knowledge discovery, and decision-making.
• Data Quality: Building reliable data workflows and ensuring that insights are based on accurate and trustworthy data.

• Programming & Querying: Python, SQL, R
• Machine Learning: Scikit-Learn, Supervised Learning, Unsupervised Learning, Predictive Modeling
• Data Analysis & Statistics: NumPy, Pandas, Statistical Analysis
• Data Visualization / Business Intelligence: Excel, Tableau, Power BI, Matplotlib, Seaborn
• Big Data / Cloud Computing: Snowflake, Databricks

❤ By Meenu