TECHNICAL TOOLKIT

Technical depth,
built for decisions.

A working stack spanning analysis, statistical modeling, machine learning, visualization, and deployment, organized around how the work moves from raw data to something people can trust and use.

111capabilities
9disciplines
100%local visuals
CORE STACK

Languages, frameworks, and delivery tools that recur across the portfolio.

ANALYSIS
Python
DATA
Pandas
NUMERICAL
NumPy
VISUAL
Plotly
PRODUCT
Streamlit
API
FastAPI
DEEP LEARNING
TensorFlow
DEEP LEARNING
PyTorch
DELIVERY
GitHub
MODEL HUB
Hugging Face
WORKING STACK

The tools matter because of how they connect.

The portfolio uses technology as a sequence: understand the data, choose the right method, validate the result, then ship the decision surface.

01

Explore

Query, clean, profile, and understand the shape of the problem before choosing a model.

02

Model

Select methods that fit the decision context, then measure them against transparent baselines.

03

Validate

Use holdouts, diagnostics, sensitivity checks, and evaluation evidence before calling a result reliable.

04

Ship

Turn the analysis into a dashboard, API, report, or workflow that a stakeholder can actually use.

CAPABILITY LIBRARY

Nine disciplines. One coherent toolkit.

Real product marks are stored locally where a branded technology has an official identity. Statistical methods and technical concepts use purpose built visual symbols rather than invented logos.

01FROM RAW RECORDS TO RELIABLE SIGNAL

Data & Analysis

Querying, wrangling, numerical computing, and distributed workflows for turning messy source data into trustworthy analytical inputs.

See GridPulse evidence
Query languageSQL
Embedded databaseSQLite
Spreadsheet analysisExcel
Programming languagePython
Data libraryPandas
Numerical libraryNumPy
Scientific computingSciPy
Distributed computingApache Spark
Distributed dataHadoop
Data warehouseApache Hive
Programming languageR
Analytical databaseDuckDB
02MODELS THAT CAN SURVIVE SCRUTINY

Statistical & Modeling

Inference, validation, segmentation, and forecasting methods used to make analytical results defensible, measurable, and decision ready.

See forecasting evidence
Statistical methodRegression
Regularized modelRidge
Ensemble modelRandom Forest
Gradient boosting libraryXGBoost
Classification modelSVM
Validation methodTrain Test Split
Dimensionality reductionPCA
ClusteringK Means
Detection methodAnomaly Detection
Anomaly detection modelIsolation Forest
Time series modelingForecasting
Experiment designA/B Testing
Statistical inferenceHypothesis Testing
Quantitative methodStatistical Analysis
Analysis methodExploratory Data Analysis
Modeling methodPredictive Modeling
03GETTING THE DATA READY TO TRUST

Data Engineering & Databases

Extraction, transformation, validation and storage work that moves data between systems and keeps it consistent before anyone reports on it.

See portfolio evidence
Extract, transform, loadETL Pipelines
Data qualityData Cleaning & Validation
Data designData Modeling
Systems integrationData Integration
Data movementData Migration
Process automationWorkflow Automation
Relational databaseMySQL
Relational databasePostgreSQL
Relational databaseSQL Server
Cloud data warehouseSnowflake
04ANALYSIS PEOPLE CAN ACTUALLY USE

Business Intelligence

Visualization, dashboarding, and application frameworks for moving analysis out of notebooks and into interfaces built for decisions.

See dashboard evidence
BI platformTableau
BI platformPower BI
Visualization libraryMatplotlib
Visualization librarySeaborn
Interactive visualizationPlotly
App frameworkStreamlit
ML interfaceGradio
API frameworkFastAPI
BI platformQlik Sense
Spreadsheet reportingExcel PivotTables & Macros
05PRACTICAL INTELLIGENT SYSTEMS

ML, NLP & LLM

Evaluation, retrieval, embeddings, language models, and fine tuning workflows for building useful generative AI systems with measurable behavior.

See LLM evaluation evidence
ML frameworkScikit learn
Model frameworkTransformers
Language modelDistilBERT
LLM frameworkLangChain
Vector databaseChromaDB
Retrieval methodRAG
Language modelLlama 3.2
Language modelMistral 7B
Fine tuning methodLoRA / QLoRA
Fine tuning frameworkPEFT
Training frameworkTRL
Evaluation metricROUGE
06STRUCTURE FROM VISUAL DATA

Computer Vision & Deep Learning

Deep learning, interpretability, detection, and segmentation techniques for extracting useful structure from images and visual data.

See vision evidence
Deep learning frameworkTensorFlow
Deep learning frameworkPyTorch
Vision architectureEfficientNetB0
Training methodTransfer Learning
InterpretabilityGrad CAM
Object detectionGrounding DINO
Generalization methodZero Shot Learning
Segmentation architectureU Net
Vision taskImage Segmentation
07WHERE THE WORK RUNS

Cloud, DevOps & Development

Cloud storage, managed databases, containers and developer tools used to host data, train models and test the services around them.

See portfolio evidence
Cloud storageAWS S3
Cloud ML platformAWS SageMaker
Managed databaseAWS RDS
Cloud platformMicrosoft Azure
Cloud platformGoogle Cloud Platform
Container orchestrationKubernetes
ScriptingPowerShell
Markup languageHTML
Code editorVS Code
Python distributionAnaconda
Interactive notebookJupyter Notebook
Cloud notebookGoogle Colab
API testingPostman
08THE DELIVERY LAYER

Tools & Platforms

Collaboration, automation, analytics, and enterprise administration tools that support delivery beyond the model itself.

See portfolio evidence
Version control and collaborationGitHub
Model hub and ML collaborationHugging Face
Project managementJira
Data science platformDataiku
CRM platformBloomerang CRM
Digital analyticsAdobe Analytics
Automation platformZapier
Cloud administrationGoogle Workspace Admin
Enterprise administrationMicrosoft 365 Admin
Productivity suiteMicrosoft Office
Team communicationMicrosoft Teams
Document managementSharePoint
Team documentationConfluence
IT service managementServiceNow
Team communicationSlack
Cloud content managementBox
09HOW THE WORK GETS DONE WITH PEOPLE

Business, Quality & Professional Skills

Performance tracking, process improvement, audit support and the working habits that keep analysis useful to the teams who depend on it.

See leadership and service
Business analyticsSupplier Performance Analytics
Quality managementQuality Metrics
Performance reportingKPI Tracking
Operations practiceContinuous Improvement
Operations practiceProcess Optimization
Process designProcess Mapping
Process designProcess Development
Controls and complianceAudit Support (SOX / SSAE)
DocumentationTechnical Writing
Professional skillProblem Solving
Professional skillTeamwork
Professional skillInterpersonal Communication
Professional skillConflict Resolution
THE POINT OF THE STACK

Tools are useful when they make the work clearer, faster, and more defensible.

Explore project case studies ↗