Chitrita Goswami, Ph.D. — Data Scientist

Data Scientist specializing in computational biology, bioinformatics, foundation models and AI/ML approaches for biological research. Experienced in developing multi-omics computational frameworks, LLM applications, and predictive models for targeted therapy. Skilled in translating complex biological data into actionable insights. As a data scientist at Roche, my portfolio includes enabling target identification, target prioritization, target assessment and gaining insights into molecular mechanism of exceptional responders treated with checkpoint inhibitors (CPI)/ Immune Checkpoint Inhibitor (ICI). I have spearheaded challenging projects spanning cancer immunotherapy, drug response, survival analysis and patient stratification, interpreting cellular heterogeneity in single-cell data. Also an experienced problem solver with financial data (demand forecasting, algorithmic trading, portfolio management, quantitative research, backtesting). My key skill-set and hands-on experience include: + Data Science and Analytics + Machine Learning (ML) & Deep Learning (DL) + Computational Biology (CB) & Bioinformatics + Multi-omics + Immunoinformatics + Biomarker Discovery & Clinical Data Insights + Large Language Models (LLM) + Retrieval Augmented Generation (RAG) + Predictive Analysis & EDA + Financial Data Analysis (+ Fintech, + Quantitative Research, + Algorithmic Trading, +Demand Forecasting) + Python & R Programming & Pytorch & TensorFlow Key Achievements: + 7 journal and conference publications + Patent holder + Built custom chat-bot that ranked in the top 10% among 1500+ submissions. + Worked on Govt. of India funded projects

Stackforce AI infers this person is a Healthcare and Fintech specialist with strong expertise in Data Science and Computational Biology.

Location: New York City, New York, United States

Experience: 9 yrs

Skills

Computational Biology
Cancer Immunotherapy
Machine Learning
Data Science
Genomics
Deep Learning
Financial Modeling
Quantitative Research

Career Highlights

Developed multi-omics frameworks for cancer immunotherapy.
Engineered LLM powered systems for biological insights.
Published 7 papers and holder of a patent.

Work Experience

Roche

Data Scientist [Cancer Immunotherapy] (3 yrs 5 mos)

Strand Life Sciences

Bioinformatics Scientist (6 mos)

Indian Council of Medical Research (ICMR)

Research Associate (6 mos)

NextOrbit

Machine Learning Consultant (7 mos)

Indraprastha Institute of Information Technology, Delhi

Doctoral Researcher (4 yrs 6 mos)

Quant One Technologies Pvt Ltd

Quantitative Research Intern (5 mos)

Education

Pharmacology Drug Development : From Molecule to Prescription at Weill Cornell Graduate School of Medical Sciences

Doctor of Philosophy - PhD at Indraprastha Institute of Information Technology, Delhi

MITx : Quantitative Biology at Massachusetts Institute of Technology

HarvardX: CS50's Introduction to Artificial Intelligence with Python at Harvard University

B.Tech at B.Tech(Computer Science and Engineering)

Chitrita Goswami, Ph.D.

Data Scientist

New York City, New York, United States9 yrs experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Developed multi-omics frameworks for cancer immunotherapy.
Engineered LLM powered systems for biological insights.
Published 7 papers and holder of a patent.

Stackforce AI infers this person is a Healthcare and Fintech specialist with strong expertise in Data Science and Computational Biology.

Contact

Skills

Core Skills

Computational BiologyCancer ImmunotherapyMachine LearningData ScienceGenomicsDeep LearningFinancial ModelingQuantitative Research

Other Skills

Generative AILarge Language Models (LLM)ProgrammingScientific WritingMulti-omicsRetrieval-Augmented Generation (RAG)Cancer GenomicsAlgorithmsAlgorithm DevelopmentCancer ResearchResearch and Development (R&D)Feature EngineeringPyTorchComputer ScienceTeaching

About

Experience

Roche

Data Scientist [Cancer Immunotherapy]

Oct 2022 – Present · 3 yrs 5 mos · New York, United States

1. Developed a multi-omics based approach to identify mechanisms of exceptional response for anti–PD‐L1 drug ( Immune checkpoint inhibitor (ICI) ) using in-house clinical trial data.
2. Developed multi-omics computational frameworks for integrating genomic, transcriptomics, targeted panels, EHR data and variant analysis, enabling robust reverse translation to elucidate patient response mechanisms.
3. Enabled target identification, in-silico target assessment with checkpoint inhibitors (CPI) data, advancing clinical trial optimization.
4. Harnessed multi-omics data to empower target enrichment.
5. Developed interpretable Machine Learning (ML) models for long-COVID diagnosis with 0.92 AUC using EHR data of 520K patients.
6. Contributed to preclinical drug development pipeline via in-silico target assessment of CDC25 biomarkers.
7. Engineered a custom vector database by applying various data wrangling methods for automated retrieval and indexing of relevant scientific literature, significantly accelerating the research processes.
8. Developed a Large Language Model (LLM) powered Retrieval Augmented Generation (RAG) system that provides insights into the activation and inhibition effects of genes on immune response.

Computational BiologyGenerative AILarge Language Models (LLM)ProgrammingScientific WritingData Science+5

Strand life sciences

Bioinformatics Scientist

Apr 2022 – Oct 2022 · 6 mos · Remote Contractor For Roche DIA

1. Contributed to computational pipeline development that enables selection of key features for blood-based early cancer detection for NSCLC and CRC.
2. Fragmentomics features are then computed for the selected features from cfDNA expression profiles, which can be used for early cancer detection using liquid biopsy.

AlgorithmsProgrammingData ScienceGenomicsAlgorithm DevelopmentCancer Research+3

Indian council of medical research (icmr)

Research Associate

Oct 2021 – Apr 2022 · 6 mos · New Delhi, Delhi, India

Predicting adverse pregnancy outcomes by leveraging deep learning methods to analyze ultrasound placental images.

PyTorchComputer ScienceProgrammingTeachingLarge-scale Data AnalysisDeep Learning+2

Nextorbit

Machine Learning Consultant

Feb 2019 – Sep 2019 · 7 mos

1. Developed machine learning models for demand forecasting for seasonal products in retail stores.
2. Resulted in a 8% revenue increase for clients by better predicting seasonal and non-seasonal trends.

OptimizationProgrammingBacktestingFinancial ModelingData Science

Indraprastha institute of information technology, delhi

Doctoral Researcher

Aug 2017 – Feb 2022 · 4 yrs 6 mos · New Delhi Area, India

My dissertation demonstrates we can leverage machine learning in translational cancer research to come up with solutions that can make the journey from laboratory into the clinic to improve patient care. Overall, my research is a significant feat to address the broader issues in precision oncology, namely that of accessibility, reproducibility, and disease specificity.
Main Projects:
1. Survival analysis and patient stratification for Multiple Myeloma patients
2. Non-invasive, blood-based, inexpensive NSCLC screening technology
3. Developing algorithm to rank features in the latent space after non-linear dimension reduction.

Computational BiologyNatural Language Processing (NLP)OptimizationComputer ScienceResearchProgramming+11

Quant one technologies pvt ltd

Quantitative Research Intern

Jan 2017 – Jun 2017 · 5 mos · India · On-site

1. Simulated numerous long-term multi-billion dollar portfolios (up to USD 2 billion) with periodic re-balancing to achieve a target return of up to 30% and target volatility of 25% p.a.
2. Decoded asset allocation strategies from white papers to optimize portfolio management.
3. Initiated research on Tactical Asset Allocation strategies at QuantOne Technologies.
4. Developed a generalized framework for back- testing low-frequency quantitative trading strategies by expanding the back-testing capabilities at QuantOne Technologies.
5. Developed advanced portfolio optimization algorithms for multi-asset ETF portfolios (energy, real estate, pharma, metal, mining, biotech), incorporating periodic rebalancing and risk constraints to maximize returns while maintaining target volatility.
6. Designed and deployed algorithms to improve Sharpe Ratio of existing asset allocation strategies by using machine learning and feature engineering techniques such as PCA.
7. Mentored students from India's top engineering colleges for developing long term asset allocation strategies.

AlgorithmsProblem SolvingOptimizationProgrammingQuantitative ResearchModel Development+15