1. Subtyping of cancer patients based on their gene mutation, RNA seq, protein expression data using various unsupervised machine learning algorithms. Also discovery of predictive biomarkers for certain clinical outcomes using supervised machine learning algorithm. 2. I was tasked with leading, guiding, and mentoring a team of 10 analysts engaged in various protocol studies, mostly in the Oncology and Cardiovascular therapeutic area. My responsibilities include expediting the analysis process and deriving actionable insights, ensuring a thorough comprehension of client requirements, and upholding the quality standards of our deliverables.3. Additionally, I am responsible for enhancing the skills of the team, including my own, through organizing diverse learning sessions covering statistical concepts and programming.

1. Was responsible for conducting comprehensive analyses aimed at elucidating the comparative risk of Immune mediated Colitis among Melanoma patients undergoing Immune Checkpoint Inhibitor (ICI) therapy versus those undergoing non-ICI treatment protocols. This project entailed close collaboration with external stakeholders to refine the clinical study's design, develop suitable algorithms for event and covariate identification, execute feasibility assessments to inform study direction, and ultimately, execute the analysis. Additionally, I played a pivotal role in crafting the abstract and designing the poster subsequently published in the AACR 2023 journal.2. Engaged in a Real World Evidence (RWE) study aimed at gaining deeper insights into the patient profile, disease progression, and associated burden of illness for pediatric patients with Hypertrophic Cardiomyopathy (HCM) in the United States (US), with the objective of informing diagnostic pathways and optimizing patient care. My responsibilities encompassed conducting feasibility analyses, engaging in discussions with advisors and stakeholders to refine study objectives, drafting the protocol, executing the protocol study utilizing Real World Data sourced from the US Health Insurance Claims and Electronic Health Records using SAS, SQL, and Python. Additionally, I contributed to shaping the abstract submitted for presentation at the ISPE 2024 conference, recognized as the leading global event in pharmacoepidemiology.3. Concurrently, I was involved in mentoring fellow team members/juniors involved in various protocol studies.4. Additionally, I led several instructional sessions covering a range of subjects, including study designs such as cohort and case-control methodologies, propensity score matching techniques, and essential programming concepts tailored to Real World Data analysts within both my team and other departments across the company.

1. Employed natural language processing (NLP) techniques to conduct sentiment analysis on the off-label usage of a biological drug manufactured by a prominent pharmaceutical company. Data acquisition involved scraping Reddit and other health forums using Python.2. Executed a rapid-response project aimed at extracting the mechanism of action and other pertinent pharmacological details of drugs utilizing an API sourced from a government website. This tool was promptly adopted by stakeholders and played a pivotal role in categorizing cancer medications, subsequently informing the design of Real World Evidence (RWE) studies.3. Led an end-to-end Real World Evidence (RWE) study to investigate the risk factors associated with cardiovascular events in Melanoma patients, employing the Cox proportional hazards model. Engaged in direct collaboration with diverse stakeholders including epidemiologists and clinicians throughout the study's lifecycle, from design and interpretation to completion.