About me
I’m a results-driven quantitative engineer with a strong foundation in data science, machine learning, and statistical modeling. I build analytical systems that transform complex operational and customer datasets into clear, actionable insights that drive real business decisions.
At Exelon, I’ve developed AI-powered affordability models, customer behavior frameworks, and large-scale data pipelines supporting regulatory, risk, and operational analytics. I also contributed to predictive modeling and quantitative research initiatives with the National Science Foundation, strengthening my background in applied statistics and experimentation.
With hands-on experience in Python, R, SQL, and cloud platforms (AWS, Azure, GCP), I design models that are accurate, explainable, and production-ready: enabling forecasting, risk assessment, segmentation, and decision optimization. I place strong emphasis on model governance, data quality, and reproducibility.
Having worked across energy, FinTech, ESG, and pharma, I bring a broad understanding of how data moves through organizations and how quantitative models influence strategy. I enjoy end-to-end problem solving from experimental design and feature engineering to deployment, visualization, and executive-level communication and I’m especially focused on building reusable frameworks and scalable analytics foundations.
What I'm doing
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Quantitative Modeling
Building predictive and scenario-based models for affordability, risk, and strategic planning using statistical and ML techniques.
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Data Engineering & Analytics
Designing robust data pipelines, feature stores, and analytics layers that power dashboards, reporting, and experiments.
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Machine Learning & AI
Applying ML and deep learning to energy, pharma, finance, and real estate problems, with an emphasis on explainability and trust.
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Cloud & Cybersecurity
Deploying workloads on AWS, Azure, and GCP with secure-by-design principles, monitoring, and cost optimization.
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Energy & Sustainability Analytics
Using data and modeling to understand customer affordability, design support programs, and enable equitable energy transitions.
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Financial & ESG Research
Exploring ESG signals, market structures, and alternative datasets to study sustainable business practices and investment decisions.
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Mentoring & Career Development
Supporting students and professionals with resume reviews, interview prep, and practical guidance on breaking into data and quant roles.
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Open-source & Community Projects
Running collaborative initiatives like Mail-Stream and Path Finder AI that bring people together to build real tools and learn by doing.
Products & Initiatives
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Mail-Stream
“Mail-Stream: Building Bridges with Code” is a collaborative program to build a free Mass Emailer for communities, creators, and teams. We combine engineering, content strategy, and outreach to create a powerful mass-emailing tool.
Visit: MailStream.com -
Path Finder AI
“Path Finder AI: Navigating Careers with Innovation” is an AI-driven Resume Analyzer initiative. We help professionals optimize their resumes and career trajectories using NLP and ML-driven insights for smarter, faster growth.
Visit: PathFinder.com
Testimonials
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Energy & Utilities Leader
“Satyam has a rare ability to connect complex quantitative models with real-world business outcomes. His work on affordability analytics and scenario modeling has materially improved how we plan and prioritize.”
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Research Supervisor
“In our pharmaceutical engineering group, Satyam consistently took ownership of end-to-end pipelines - data, modeling, and interpretation. His curiosity, rigor, and documentation set a high bar for the rest of the lab.”
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FinTech Collaborator
“Working with Satyam on alternative lending data was like working with a full team. He handled messy data, built robust models, and communicated trade-offs clearly to both technical and non-technical stakeholders.”
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Student Mentee
“Satyam’s mentorship helped me move from being overwhelmed by data roles to having a clear roadmap. His feedback on projects and resumes is both honest and extremely actionable.”
