DATA SCIENTIST & ANALYTICS EXPERT
Who Am I ?
I am a Data Scientist with an M.Sc. in Data Science (Statistics) from TU Dortmund, specializing in automation, data pipeline optimization, and predictive modeling. My expertise spans statistical learning, machine learning for high-dimensional data, NLP, bioinformatics, and big data analytics.
With professional experience at RWE, KPMG, and Accenture, I have practical skills in Python, R, SQL, Azure (CI/CD, Databricks), AWS Cloud, and data visualization tools like Tableau, Power BI, Elasticsearch, and Kibana. I am driven by transforming complex datasets into actionable insights that support strategic and operational decision-making.
Education:
• M.Sc. in Data Science (Statistics) - TU Dortmund, Germany (2020-2025)
• B.Tech. in Computer Science - GGSIPU, New Delhi (2013-2017)
Technical Expertise
Python, R, SQL, Java, C++, JavaScript - Expert in statistical computing and software development
Deep Learning, Neural Networks, TensorFlow, PyTorch, Regression, Classification, Time Series Analysis
ETL Pipelines, PySpark, Databricks, Docker, Kubernetes, PostgreSQL, MongoDB, Oracle
Azure (CI/CD, Databricks), AWS (S3, Lambda), Power BI, Tableau, Elasticsearch, Kibana
Featured Work
Building a React and Dash-based dashboard with multi-format file ingestion, dynamic visualizations, and an AI-enabled chatbot using LangChain for contextual search and insights.
Master Thesis: Developed a simulation-based research framework to benchmark similarity measures, deriving statistical power curve analysis to assess sensitivity and robustness in high-dimensional data environments.
Developed FFT and periodogram-based predictive models for real-time noise diagnostics and early-warning systems for chatter detection in stochastic drilling processes.
Applied multinomial logistic regression and geospatial modeling to analyze regional hypertension risk patterns based on ESC/ESH clinical guidelines, integrating demographic and health datasets.
Simulated D-optimal and ED-optimal experimental designs for Emax, Log-linear, and Exponential models to improve robustness of dose-response modeling under parameter uncertainty.
Implemented time-series regression with anomaly detection and data augmentation to forecast long-term windmill efficiency degradation and optimize maintenance schedules.
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My Professional Journey
Dec 2023 – Mar 2025
Dec 2017 – Mar 2020
Jul 2022 – Aug 2022
Jul 2021 – Jul 2022
May 2016 – Jun 2016
Recognition & Leadership
Q4 2019 | Accenture
Outstanding performance in developing ML-powered solutions for BMW manufacturing optimization
2016 | HP Training Program
Recognizing exceptional technical skills and leadership qualities
2016 | Technical & Cultural Fest
Demonstrated organizational and leadership capabilities
Community Lead
2022 – Present
Leading initiatives to foster AI and data science education and collaboration. Organizing workshops, mentoring sessions, and knowledge-sharing events within the global community.
Member
2018 – 2019
Actively participated in corporate social responsibility initiatives, contributing to community development projects and social impact programs.
Member
2014 – 2015
Engaged in professional development activities and technical knowledge exchange with fellow computer science professionals.
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