Satyam Pandey

  Breaking Into Quant & Data Roles: A Practical Roadmap A candid guide I share with mentees on projects, skills, and storytelling that actually move the needle in interviews Breaking into quant and data roles can feel confusing for one reason: there are too many paths, too many resources, and too many opinions. Some people tell you to master math first. Others say build projects. Some recommend grinding interview questions. Others say networking matters most. The truth is simpler and more helpful: You do not need to do everything. You need to do the right things in the right order. This roadmap is a practical guide I share with mentees who want to break into roles like: Quantitative Analyst / Quant Research Data Analyst / Business Analyst Data Engineer Machine Learning Engineer Analytics Engineer Risk / Pricing / Forecasting Analyst Whether you are a student, switching careers, or leveling up from a current role, this blog will help you build a structured plan that leads to r...

  Designing Robust Data Pipelines for Analytics Teams Key patterns and trade-offs when building pipelines that support both BI and advanced modeling workloads Data is only valuable when it is reliable, usable, and delivered on time. Most organizations do not suffer from a lack of data. They suffer from fragile pipelines, mismatched definitions, slow refresh cycles, and dashboards that no one fully trusts. That is why robust data pipelines are the foundation of modern analytics . When pipelines are built well, analytics teams move faster, experiments are safer, reporting becomes consistent, and leadership decisions become more confident. When pipelines are built poorly, the organization ends up in a cycle of broken dashboards, last-minute fixes, and constant confusion about which numbers are correct. In this blog, I will break down how to design robust data pipelines for analytics teams, including architecture choices, core design patterns, best practices, and the real trade-offs y...

Lessons From Working Across Cyberpsychology, BioTech & FinTech Labs What collaborating with diverse research teams taught me about reproducibility , documentation , and knowledge transfer Working in one research domain teaches you depth. Working across multiple labs teaches you something equally valuable: how to think clearly, communicate consistently, and build systems that other people can trust. In 2023, I had the opportunity to collaborate across three very different research environments: Cyberpsychology, BioTech, and FinTech . Each lab had its own language, workflows, tools, priorities, and success metrics. What counted as “good work” in one space looked completely different in another. But across all of them, three things kept showing up as the real difference between chaos and progress: Reproducibility Documentation Knowledge transfer This blog is a reflection on what I learned from working across these domains and how those lessons apply beyond academia. Whether you are do...

  Raw Data to Executive Dashboard A practical walkthrough of how I structure projects end-to-end: data design, modeling, and visual storytelling for leaders Most dashboards fail for one simple reason: they look impressive, but they do not answer the questions leaders actually ask. Executives do not want a wall of charts. They want clarity. They want to know what is happening, why it is happening, what will likely happen next, and what decision they should make today. That is why building an executive dashboard is not only a visualization task. It is an end-to-end analytics process that starts with messy raw data and ends with a story that drives action. In this blog, I will walk through the exact approach I use to take a project from raw data to a leadership-ready dashboard. This includes data discovery , modeling , KPI design , dashboard structure, and the storytelling layer that makes the insights stick. If you are building dashboards for leadership, this workflow will help yo...

  Predicting Drug Compound Ratios With Deep Learning Lessons from developing ML models at CIMSEPP to optimize API and excipient formulations Formulating a drug is not just chemistry. It is an engineering problem that balances performance, stability, safety, manufacturability, and cost. At the center of that challenge sits a deceptively complex question: What is the right ratio of active pharmaceutical ingredient (API) to excipients to create a formulation that works reliably every single time? Traditionally, answering this involves iterative lab experimentation: mix, test, adjust, repeat. While this approach is scientifically sound, it is also expensive, slow, and often limited in the number of combinations a team can realistically explore. This is where deep learning can help. Deep learning does not replace formulation science. Instead, it can act as a powerful accelerator by learning patterns from historical experiments and predicting promising compound ratios before committing...