Juliia N

Co-Founder & Chief Scientific Officer (CSO)

Mar 2025 - Present • 1 yr 5 mos

As Co-Founder and CSO of Forneus Technologies, I lead the biological and data strategy behind our bioinformatics and AI-driven HealthTech initiatives — including antimicrobial resistance (AMR) tracking, genomic data workflows, and literature-mining AI agents (RAG systems). I translate complex life-sciences and microbiology domain knowledge into clear, reproducible computational pipelines, bridging biological research with modern AI/data engineering. Key focus areas: differential gene expression analysis, single-cell transcriptomics, clinical variant annotation, and building reproducible bioinformatics workflows for research and biotech partners.

Statistical Data Analyst

Jan 2019 - Feb 2023 • 4 yrs 1 mo

As a Researcher and Data Analyst at the Department of Biochemistry and Microbiology, I bridged the gap between complex life sciences and rigorous data science. My primary focus was to ensure that all experimental and clinical data was collected, cleaned, and analyzed with the highest level of academic strictness, guaranteeing that research conclusions were mathematically sound and reproducible. Key Responsibilities & Achievements: • Advanced Statistical Analysis: Designed and executed hypothesis testing for complex biological datasets. Ran extensive parametric and non-parametric tests (ANOVA, t-tests, Chi-square, Mann-Whitney U) to validate experimental outcomes. • Deep Data Cleaning: Handled messy, real-world laboratory and clinical data. Identified data artifacts, handled missing values through mathematically precise imputation (avoiding generic averaging), and ensured absolute data integrity before analysis. • Publication-Quality Visualization: Developed high-resolution (300 dpi), presentation-ready charts (box plots, violin plots, correlation matrices) that effectively communicated complex trends. These visualizations were tailored to meet the strict standards required for peer-reviewed scientific journals. • Experimental Design & A/B Testing: Assisted in structuring experimental groups (control vs. treatment) to ensure statistically significant sample sizes. Calculated effect sizes (e.g., Cohen's d/h) to measure the true impact of variables rather than relying solely on p-values. • Scientific Reporting: Translated raw data spreadsheets into clear, structured statistical reports. Documented methodology, assumption checks (normality, variance), and final metrics to support research publications. By applying this level of statistical rigor to life sciences, I developed a zero-tolerance policy for "statistical noise" and false trends—a strict methodology I now apply to business, clinical, and marketing data analytics.