Actively seeking roles in data science and machine learning; I am keen to leverage my expertise in these fields to drive insights in various industries such as biotechnology and life sciences. Graduating with a B.S./M.S.E dual degree in Biomedical Engineering and Computer Science at Johns Hopkins University in May 2024.


  • Data Science
  • Engineering
  • Software development


Rishika Vadlamudi

  • Biomedical Engineering

Rishika Vadlamudi

Work experience
  • Performance Analyst at   Pfizer
  • Aggregated and manipulated clinical trial datasets in Alteryx in the Operational Analytics and Quantitative Sciences group within Clinical Development & Operations, while tackling ad-hoc data mining and manipulation requests.
  • Developed an interactive dashboard with visualizations in Spotfire, identifying key indicators of low performing clinical trial sites, to then run ML algorithms through DataIKU DSS and extract insights and data correlations
  • Systematized clinical trial dataset columns in Alation, enhancing data source tracking and comprehension.
  • Built workflows using SQL in Alteryx that performed cycle time calculations and classifications to populate clinical trial datasets according to organization specific definitions and standards.
  • Provided analytical support into clinical trial site operational performance at the study and country level by optimizing data visualizations in Spotfire dashboards and simplifying Alteryx data analysis workflows.

Rishika Vadlamudi

Work experience
  • Worked in various computational labs:
  • Conducting a Genome-Wide Association Study (GWAS) through the design and implementation of bioinformatics pipelines, harnessing Python, autoML techniques, and statistical modeling to pinpoint genetic markers for tuberculosis infection sites.
  • Applying statistical machine learning strategies to assimilate lineage markers and resistance profiles to improve the accuracy of genetic linkage assessments in tuberculosis research.
  • Analyzed signal and image processing data obtained from a miniature multi-contrast microscope for functional imaging in freely behaving animals in ImageJ Fiji.
  • Built a statistical regression analysis program extension in Python for Fiji data analysis tool to develop stronger imaging capabilities in the lab.
  • Produced Python scripts using NumPy and Pandas libraries to construct brain signaling plots and analyze event induced changes to cortical responses in mice from data aggregated by micro-electrode array electrical readings.

Rishika Vadlamudi

  • Biomedical Engineering, Computer Science

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