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Recent MSc graduate in Data Science from the University of Helsinki with hands-on experience in Python, SQL, Power BI, and reproducible data analysis on large-scale longitudinal and tabular datasets. Skilled in turning data into clear business insights, building structured analytical workflows, and communicating results to technical and non-technical stakeholders. Interested in applying analytics, automation, and AI-enabled tools to support profitable decision-making, reporting quality, and operational efficiency. Experienced in cohort construction, temporal feature engineering, statistical modeling, and machine learning approaches for evidence-based research and practical next steps.…Recent MSc graduate in Data Science from the University of Helsinki with hands-on experience in Python, SQL, Power BI, and reproducible data analysis on large-scale longitudinal and tabular datasets. Skilled in turning data into clear business insights, building structured analytical workflows, and communicating results to technical and non-technical stakeholders. Interested in applying analytics, automation, and AI-enabled tools to support profitable decision-making, reporting quality, and operational efficiency. Experienced in cohort construction, temporal feature engineering, statistical modeling, and machine learning approaches for evidence-based research and practical next steps.

Dhyey Sampat

Data Scientist, Data Analyst, Data Visualizer, +2

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Recent MSc graduate in Data Science from the University of Helsinki with hands-on experience in Python, SQL, Power BI, and reproducible data analysis on large-scale longitudinal and tabular datasets. Skilled in turning data into clear business insights, building structured analytical workflows, and communicating results to technical and non-technical stakeholders. Interested in applying analytics, automation, and AI-enabled tools to support profitable decision-making, reporting quality, and operational efficiency. Experienced in cohort construction, temporal feature engineering, statistical modeling, and machine learning approaches for evidence-based research and practical next steps.…Recent MSc graduate in Data Science from the University of Helsinki with hands-on experience in Python, SQL, Power BI, and reproducible data analysis on large-scale longitudinal and tabular datasets. Skilled in turning data into clear business insights, building structured analytical workflows, and communicating results to technical and non-technical stakeholders. Interested in applying analytics, automation, and AI-enabled tools to support profitable decision-making, reporting quality, and operational efficiency. Experienced in cohort construction, temporal feature engineering, statistical modeling, and machine learning approaches for evidence-based research and practical next steps.

Available to hire

Recent MSc graduate in Data Science from the University of Helsinki with hands-on experience in Python, SQL, Power BI, and reproducible data analysis on large-scale longitudinal and tabular datasets. Skilled in turning data into clear business insights, building structured analytical workflows, and communicating results to technical and non-technical stakeholders.

Interested in applying analytics, automation, and AI-enabled tools to support profitable decision-making, reporting quality, and operational efficiency. Experienced in cohort construction, temporal feature engineering, statistical modeling, and machine learning approaches for evidence-based research and practical next steps.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Finnish

Beginner

Work Experience

Research Assistant at Institute for Molecular Medicine Finland (FIMM), DSGE Lab

October 1, 2025 - April 30, 2026

Standardized large-scale data workflows across ~500,000 FinnGen patients and 5 million ML4HEALTH records, improving consistency and reproducibility in longitudinal analysis. Built structured pipelines for cohort construction, censoring rules, endpoint definitions, and temporal feature engineering, reducing manual data preparation and improving data quality. Developed and compared analytical models using Cox regression, XGBoost, and Transformer-based sequence models to support evidence-based decisions across experimental settings. Collaborated with interdisciplinary stakeholders to translate analysis results into clear follow-up questions, practical next steps, and validation priorities.