Data Scientist Science

SHRINIDHI RAJESH, MS, GStat

Statistics Data Science Machine Learning

Results-driven Data Scientist with a Master's in Applied Statistics and GStat accreditation, transforming complex data into critical insights. Demonstrated expertise in delivering high-impact solutions that drive organizational performance and innovation. Committed to pushing analytical boundaries and solving critical business challenges. EDUCATIONAL QUALIFICATIONS

Master of Science in Applied Statistics DePaul University January 2023 - November 2024 Chicago Minor in Biostatistics and Data Science – GPA: 4.0/4.0 Graduated with Distinction Bachelor of Technology in Bioinformatics SASTRA University June 2013 - May 2017 Thanjavur EXPERIENCES

Data Scientist One Tail at a Time January 2025 – Present Chicago

• Redesign data infrastructure with a streamlined pipeline (Amazon S3, PostgreSQL, KNIME, Figma), replacing manual Excel tasks and establishing a single source of truth for efficient data management and modeling.

• Build predictive models (Regression, Clustering, Time Series, Survival Analysis) to forecast adoption success and flag at-risk cases, enabling better placements and timely interventions.

• Develop a volunteer analytics framework using statistical methods and activity-based tiering, identifying retention patterns to optimize onboarding, recognition, and scheduling.

• Apply NLP to open-ended responses from volunteer surveys, adoption applications, and return forms, uncovering recurring themes that shape adopter experience and strengthen engagement.

• Create Power BI dashboards to visualize trends in adoption and volunteer metrics, providing actionable insights and trustworthy reporting tools, shifting organizational culture towards proactive, insight-driven decision-making. Statistician DePaul University January 2023 – April 2025 Chicago

• Delivered actionable insights across 17 data projects over 2.5 years, leveraging R, SAS and Python to streamline analytical workflows and accelerate outcomes that shaped strategy, funding priorities, and operational planning.

• Applied linear mixed-effects models and other statistical techniques to assess nurses' intubation performance, leading to updated airway management protocols in critical care, boosting patient outcomes and staff efficiency.

• Formulated and evaluated 125+ predictive models to assess financial instrument attributes, refining audit risk assessment procedures to yield accurate forecasts.

• Performed factor analysis, regression, and sentiment analysis on mental health survey data, identifying key drivers of student experience, which led to a 10% increase in student satisfaction through targeted programs.

• Translated complex statistical findings into clear reports and visualizations, helping clients grasp data insights by approximately 30% more effectively and supporting more informed strategic decision-making. Technical Lead IBM February 2018 - August 2021 Bangalore

• Led the development and automation of data solutions, Enterprise Compliance Management, elevating malware detection and reducing configuration drift risks by 40% across APAC, strengthening security and operational resilience.

• Built internal tools with Python, SQL, Perl, and HTML to automate routine tasks, enhancing system security and reducing manual workload.

• Streamlined CI/CD pipelines with Ansible, driving a 25% increase in data ingestion and model training by automating and managing infrastructure.

• Achieved 23% faster data retrieval and higher reporting accuracy by designing SQL schemas, tuning queries, and implementing stored procedures.

• Facilitated collaboration with teams across North America and Europe to advance audit compliance tracking, resulting in a 12% gain in compliance scores through better team coordination and process alignment.

• Managed end-to-end data migration to IBM Cloud and integrated Git with Travis CI for automated deployment, accelerating delivery timelines and minimizing errors.

• Maintained 100% uptime for enterprise frameworks during the IBM-Kyndryl split, ensuring uninterrupted service delivery during the critical organizational transition.

• Optimized DevOps workflows using Docker and advanced preprocessing techniques, improving application security, accelerating deployment, and enhancing compliance tracking. 312-***-****

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www.linkedin.com/in/shrinirajesh

www.shrinirajesh.com

PROJECTS

An Advanced RAG System for Sustainable Technologies September 2024 - December 2024

• Constructed a Retrieval-Augmented Generation (RAG) system to extract tactical insights from 20,000+ clean technology news articles, expanding access to sustainability intelligence.

• Implemented NLP techniques including data preprocessing, spaCy-based tokenization and semantic analysis, and integrated OpenAI Turbo 3.5 with LangChain to sharpen document understanding.

• Ensured high-quality summarization and information retrieval for clean tech insights by evaluating system performance using RAGAS, ROUGE, Perplexity, and BERTScore and quantitative assessments. A Data-Driven Population-Based Targeted Intervention for Diabetes April 2024 - June 2024

• Focused on early diabetes detection and prevention using a population health approach on 250,000+ patient records.

• Trained ML models in scikit-learn and used Pandas to process data, build risk dashboards, and align outputs with CDC guidelines.

• Enabled timely identification of at-risk individuals and supported program planning through measurable gains in early detection and community-level intervention outcomes. SKILLS

Programming Languages

• Python, R, SAS, SQL, Perl, HTML

Libraries & Frameworks:

• Statistical & Data Science: numpy, pandas, seaborn, matplotlib, scipy, statsmodels, caret, MASS, rjags, glm, tidyverse

• Machine Learning & AI: scikit-learn, PyTorch, spaCy

• Time Series: forecast, prophet

• Survival Analysis: survival, lifelines

• Mixed Effects Models: nlme, lme4

Techniques & Methodologies:

• Predictive Modeling: Linear Regression, Multivariate Regression, Generalized Linear Models, Classification, Time Series and Forecasting, Bayesian Inference, Ensemble Methods

• Data Mining: Data Preprocessing, Principal Component Analysis, Feature Engineering, Clustering

• Experimentation and Evaluation: Randomized Controlled Trials, A/B Testing, Cross Validation

• Biostatistics: Survival Analysis, Drug Design & Clinical Research, Epidemiology, Genomic Data Science

• Machine Learning & AI: Deep Learning, Natural Language Processing (NLP), Generative AI (GenAI), Large Language Models (LLMs) Tools & Software:

• Programming & Analysis: Jupyter Notebook, Anaconda

• Reporting and Visualization: Power BI, RShiny

• Cloud Services: AWS, Azure Machine Learning Studio, Azure AI

• Version Control: GitHub

• API Integration: Postman

Databases & Data Management

• PostgreSQL, IBM DB2

CERTIFICATIONS

• NVIDIA Certified Associate: Generative AI and LLMs March, 2025

• Graduate Statistician (GStat) Accreditation American Statistical Association, ASA January 2025

• Azure AI Fundamentals Microsoft December 2024

• Data Science Professional Data Camp August 2024

• AI in Healthcare Stanford Online July 2024

HONORS AND AWARDS

• Mathematical Scholarship Award DePaul 2023, 2024

• SPARKS Award IBM August 2020

• Delivery Excellence Award IBM August 2021

• Manager’s Choice Award IBM 2019, 2020, 2021

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