Deepali transitioned from fundamental physics research to applied data science, leveraging 12+ years of expertise in analyzing massive datasets and building ML pipelines. She combines rigorous scientific methodology with modern ML/DL techniques to solve complex real-world problems in healthcare, finance, and sustainability.
Route to AI Engineer, Data Scientist, Machine Learning Engineer teams building high-throughput services in the candidate's core stack.
Large-scale data processing and analysis (terabytes to petabytes). Machine learning & deep learning model development (Scikit-learn, Keras, XGBoost, LSTM, CNN). Scientific computing and statistical analysis with rigorous methodology. Data pipeline architecture (Bash, Perl, C++, SQL) on HPC clusters.
Data Engineer, Data Scientist, Machine Learning Engineer, Analytics Engineer.
On-site. Based in Setauket, US.
Targeting $130K+. Currently: Research Scientist.
Recruiter screen → hiring-manager review → technical conversation around Cosmic Muon Monitoring System, Jefferson Lab RICH Detector Analysis, ML Classification Projects.
Deepali's work in Data Engineer, Data Scientist, Machine Learning Engineer translates directly into adjacent domains with similar architecture, scale, and reliability needs.
The next pages convert the resume into a reusable proof index: production systems, platform architecture, org-wide pattern ownership, and technical leadership.
This reusable proof index maps common senior/staff engineering requirements to candidate proof. Role-specific addenda extend — not replace — this base matrix.
Requirement: Deliver on cosmic muon monitoring system at production scale.
Proof: Built automated daily monitoring pipeline (cron-based) predicting solar activity and space weather across multiple detector locations
Requirement: Deliver on jefferson lab rich detector analysis at production scale.
Proof: Processed terabytes of experimental data using HPC clusters; published results in leading peer-reviewed journal
Requirement: Deliver on ml classification projects at production scale.
Proof: Delivered multiclass classification models (Random Forest, XGBoost) and CNN-based image classification achieving high recall metrics
Requirement: Deliver on phenix experiment data analysis at production scale.
Proof: Analyzed petabytes of heavy-ion collision data; published as primary author in leading physics journals
Requirement: Ship work that requires large-scale data processing and analysis (terabytes to petabytes).
Proof: Demonstrated at prior roles.
Requirement: Ship work that requires machine learning & deep learning model development (scikit-learn, keras, xgboost, lstm, cnn).
Proof: Demonstrated at prior roles.
github.com/deepssharma — public repositories, contribution activity, and open source project surfaces.
https://gethiringfunnel.com/u/deepali-sharma
Hosted candidate portfolio with positioning, proof points, and conversion kit surface for recruiter routing.
www.linkedin.com/in/deepali-sharma-a83a126
Professional profile and recruiter connection path.
deepssharma.github.io
Additional portfolio and technical writing surface.
This kit is designed to survive internal forwarding. The fastest next step is a recruiter screen, hiring-manager review, referral handoff, or direct call.
Primary fit: AI Engineer, Data Scientist, Machine Learning Engineer.
Ask about availability to discuss a senior/staff engineering opportunity.
Handoff email with routing context for internal referral.
Direct phone action for recruiter screen or same-day routing.
Persistent candidate surface for routing, resume, and proof surfaces.
Professional profile and recruiter connection path.
Public engineering surface, repositories, and OSS project path.
Deepali Sharma is a Setauket, US-based AI Engineer. Track record across 0 roles with expertise in . Best fit: AI Engineer, Data Scientist, Machine Learning Engineer.