Experience

Lead Data Scientist – Generative AI

October 2024 – Present

• Developed and integrated core generative AI features into AlignAI’s platform, including a chatbot for automated use-case documentation and an assistive agent for generating technical, client-specific educational playbooks.
• Consulted on machine learning and AI best practices for a major automotive manufacturer, contributing to model development, deployment planning, and process documentation for a vehicle quality forecasting system.
• Collaborated cross-functionally with internal and external teams to ensure modeling and AI efforts aligned with business goals, infrastructure constraints, and user needs.

Senior Data Scientist – Generative AI

April 2024 – October 2024

• Led multiple ML consulting engagements, including the design and implementation of predictive pipelines for a major auto manufacturer to estimate both parts availability and vehicle delivery timelines to dealerships.
• Worked directly with engineering and operations teams to define model requirements, validate data pipelines, and support deployment across production environments.
• Guided external companies on implementing end-to-end ML technology stacks and AI strategies while also supporting their internal upskilling and the creation of technical enablement resources.
• Developed AI strategy frameworks and recommendations to help client organizations establish long-term AI capabilities and MLOps best practices.

Lead AI Developer and Co-Founder

March 2023 – April 2025

• Led the end-to-end development of a generative AI–driven sales enablement assistant designed to support sales professionals in strategy, execution, and goal attainment through LLM-based automation and intelligent workflows.
• Built a fully functional Slack-integrated product leveraging ChatGPT, custom query classification models, and a modular prompt orchestration layer to deliver contextualized and role-specific sales insights.
• Architected and implemented the full backend stack, including prompt delivery logic, query routing infrastructure, data ingestion pipelines, and execution environments for model testing and deployment.
• Owned the complete technical lifecycle—from rapid prototyping to production deployment—while continuously aligning the product roadmap with product feedback and evolving market needs.

Senior Data Scientist

September 2021 – April 2024

• Led the design and implementation of Nielsen’s first ML-driven meter fault classification system, modeling raw minute-level audio signal data and device metadata to detect hardware anomalies; cutting issue resolution time from 14 days to under 3 days and replacing legacy logic with a scalable and explainable ML solution.
• Engineered a fully automated pipeline monitoring tool using Python, Spark, and Airflow that processed 10+ GB of data daily across 10+ environments and automated over 50 critical diagnostic workflows, which reduced multi-day manual analysis to runtimes measured in minutes.
• Achieved a 60x speedup in data processing by reengineering Python-based workflows across CLI and Databricks environments, resultantly cutting runtime on a 200+ GB dataset from hours to just 83 seconds for nationwide household meter analysis.
• Collaborated with the Audio Measurement team to develop A/B testing infrastructure and evaluation metrics for pipeline stability, enabling second-level granularity and gaining executive visibility through high-impact data initiatives.
• Assessed and validated a high-volume (3+ GB/day) streaming data source for integration into core crediting systems by building in-house visualization tools, quality assurance frameworks, and uncovering key optimization opportunities.

Data Science, Machine Learning & Statistics Consultant

August 2020 – October 2021

• Consulted on 150+ academic and applied research projects across UNC Chapel Hill, supporting graduate students, post-docs, faculty, and staff in the implementation of rigorous data science and statistical workflows.
• Provided technical guidance on research design, regression modeling, mediation and moderation analysis, and machine learning in R, Python, Tableau, and SPSS.
• Recognized as one of the institute’s top statistical consultants for both technical expertise and instructional clarity.

Adjunct Faculty – Master of Applied Data Science

August 2024 – Present

• Delivered two semesters of DATA 785: Deep Learning, guiding Master’s-level students through advanced architectures including CNNs, RNNs, attention mechanisms, Transformers (BERT, T5), and modern LLMs (ChatGPT, Llama, Claude, Gemini).
• Taught DATA 780: Introduction to Machine Learning, covering supervised and unsupervised learning techniques, regression, classification, decision trees, and introductory neural networks, with a dual emphasis on applied implementation and underlying mathematical theory.
• Created and delivered hands-on coding labs, case-based assignments, and lecture content aimed at bridging theoretical foundations with real-world deployment challenges, while emphasizing reproducible ML workflows, responsible AI practices, and practical evaluation techniques.

Data Science Lead Instructor & Technical Coach – DS4A: Empowerment

April 2021 – April 2024 | 5 Programs

• Guided 50+ teams (300+ fellows) across five cohorts through full-cycle data science and machine learning projects, including data collection, data wrangling, model development, evaluation, and deployment.
• Supported real-world and socially impactful projects such as measuring redlining’s effect on health outcomes, analyzing disparities in COVID vaccine disbursement, and applying deep learning computer vision techniques to detect melanoma in BIPOC individuals.
• Provided consistent technical leadership and instruction across diverse domains and became one of the longest-serving and most recognized instructors in the program.

Data Engineering Lead Instructor & Technical Coach – DS4A: Data Engineering

February 2022 – July 2023 | 2 Programs

• Advised 16+ project teams (80+ fellows) in building industry-level data pipelines and data products from raw data ingestion and ETL to ML model integration and deployment using tools like S3, EC2, Lambda, and Redshift.
• Led instruction in applied data engineering topics including SQL, PostgreSQL, MySQL, Presto, Spark, and PySpark, and enabled learners to move from foundational concepts to full end-to-end data engineering solutions using modern tools and cloud infrastructure.

Machine Learning Lead Instructor – DS4A: Colombia Data Science

March 2022 – August 2022

• Delivered 6 lecture sessions to an audience of 1800+ data professionals, with topics focusing on data cleaning, univariate analysis, interaction effects, normalization, regularization, and advanced recommender systems.
• Recognized for high instructional quality and technical depth in a large-scale international program.

Data Analytics Lead Instructor & Curriculum Consultant – Amazon Analytics & Data Science

June 2021 – December 2021

• Designed and taught real-world case studies in analytics and data science using Excel, SQL, Python, and Tableau.
• Helped shape the 14-week curriculum that had so much success that it expanded from 70+ fellows to over 600 and became one of Amazon’s most successful internal upskilling programs.

Adjunct Instructor

August 2019 – October 2021

• Taught five semesters of “Introduction to Media Ethics,” with 20–30 undergraduates per cohort.
• Created and delivered lectures, writing assignments, midterms, finals, and updated course content each semester.
• Focused on strengthening students’ critical thinking skills, awareness of cognitive biases and logical fallacies, and ability to construct nuanced ethical arguments.

Adjunct Lecturer – Environmental Engineering

May 2017 – June 2018

• Developed and delivered online courses in Building Automation and Green Building Systems.
• Integrated systems thinking throughout course design to help students understand interdependencies across mechanical, electrical, and environmental building systems.
• Designed new curriculum modules, in-depth technical assignments, and evaluation tools while managing communication and grading for all enrolled students.

ML, NLP & LLM Mentor

August 2023 – August 2024

• Mentored junior data professionals through the development of an end-to-end generative AI project, focused on training a creative writing assistant in the style of Spanish literary figures.
• Advised on model architecture, fine-tuning techniques, prompt engineering, and evaluation metrics while supporting mentees' personal growth and confidence in AI development.

Machine Learning & Data Science Mentor

February 2023, February 2024

• Provided project mentorship and technical support to interdisciplinary teams during the Measure of Music hackathon, focusing on the application of ML and data science to music-tech problems.
• Supported teams working on music classification, recommendation systems, and streaming data analytics using Python, NLP, and model evaluation techniques.

LLM Instructor – Data Hour

January 2024

• Delivered a live instructional session for Analytics Vidhya’s “Data Hour” series, focused on the capabilities, limitations, and real-world implementation of large language models (LLMs) in applied settings.
• Engaged a global audience of data practitioners through a mix of theoretical insights and hands-on code walkthroughs, emphasizing practical uses of generative AI systems.

Speaker and Alumni Presenter

October 2022, March 2023

• Selected as a returning alum to speak at multiple sessions of Google’s CSRMP, sharing insights into industry research, data science careers, and LLM development practices.
• Participated in panel discussions and delivered presentations to help mentees better understand pathways into technical roles and research-aligned AI work.

Instructor & Mentor

June 2021 – June 2022

• Designed and delivered an advanced data science curriculum for over 150 undergraduate students, covering exploratory data analysis, inferential statistics, regression, NLP, and deep learning.
• Provided mentorship to students pursuing data science careers, helping them build project portfolios, strengthen technical skills, and prepare for industry interviews.

Teaching Assistant – Data Science, Machine Learning & Deep Learning

August 2019 – August 2021

• Provided instructional support across eight technical courses covering Python programming, applied machine learning, and deep learning using TensorFlow, Keras, CNNs, RNNs, and Transformers.
• Guided 50+ course participants per cohort through core ML algorithms and software development practices including OOP, PEP8, and modern coding standards.
• Consistently recognized as a top TA and selected for advanced instructional roles due to depth of technical knowledge and learner support.

Teaching Assistant

June 2017 – May 2018

• Supported high school computer science education by serving as a volunteer TA in classrooms, helping students build foundational programming and problem-solving skills in Python and block-based tools.
• Provided 1-on-1 assistance, helped debug code, and supported the lead instructor in delivering technical content to diverse learners with varying skill levels.

Agrivoltaics Computational Modeling Research Assistant

January 2015 – September 2015

• Developed a computational modeling framework in R to analyze micro-environmental conditions under solar panels using temperature, moisture, irradiance, and crop production data.
• Modeled co-located agricultural and solar energy systems to evaluate productivity, climate resilience, and environmental trade-offs under real-world conditions.

GIS Analyst & Research Assistant

January 2014 – June 2014

• Collected and analyzed 10 years of satellite and municipal hydrological data to model the effects of urban infrastructure on runoff and flooding in Binghamton, NY.
• Built geospatial visualizations and scenario-based infrastructure models using ArcGIS and MATLAB (via SQL integrations) to inform green infrastructure planning.

Environmental Data Analyst & Research Assistant

September 2013 – June 2014

• Led data collection, processing, and analysis of 20 years of weather and avian field data to investigate links between climate change and crow nesting behavior.
• Performed exploratory analysis and regression modeling on NOAA historical data and field notes to contribute to long-term ecological research studies.