[DATA SCIENCE]
Models That Predict.
Systems That Act.
We build ML and AI systems grounded in real operational context — not just notebook experiments. From forecasting engines and NLP pipelines to risk classifiers and simulation platforms, our data science practice delivers models that are production-deployed, monitored, and continuously improved.
>85%
Weekly Forecast Accuracy
ML-powered operations volume prediction
±5%
AHT Confidence Interval
Content moderation policy impact forecasting
18%
Overstaffing Reduction
Smart mobility workforce optimization
[WHAT WE BUILD]
Six Data Science
Capabilities.
Production-deployed models. Monitored, retrained, explainable. We don't do one-off experiments — we build ML systems that operate continuously in the real world.
01
Predictive & Prescriptive Modelling
Build supervised learning models that forecast outcomes and recommend actions — from customer churn and attrition risk to demand forecasting and operational anomaly detection. Deployed with monitoring, confidence intervals, and explainability outputs.
Random ForestXGBoostLogistic RegressionSHAP
02
Time Series Forecasting
Production-grade forecasting systems for volume prediction, demand planning, and operational scheduling. Ensemble approaches combining classical (Prophet) and deep learning (LSTM) methods with automated daily retraining pipelines and accuracy tracking.
XGBoostFacebook ProphetLSTM (Keras)ARIMA
03
NLP, Sentiment Analysis & Topic Modelling
Extract signal from unstructured text at scale — employee survey responses, customer feedback, support tickets, and social content. Sentiment scoring, topic clustering, and trend detection pipelines designed for operational decision-making.
spaCyNLTKBERTLDA / BERTopicTextBlob
04
What-If Scenario Simulation
Interactive simulation interfaces that let business users model the downstream impact of policy changes, capacity decisions, and operational scenarios before committing. Monte Carlo simulation, sensitivity analysis, and scenario comparison tooling.
Monte CarloStreamlitPlotly DashPython
05
MLOps: Monitoring, Drift & Retraining
Productionise models with full observability — performance dashboards, data drift detection, automated retraining triggers, and model versioning. We build the operational infrastructure that keeps models accurate as the real world changes around them.
AWS SageMakerMLflowGitHub ActionsGreat Expectations
06
Population Health Analytics & Risk Stratification
Clinical risk stratification models for identifying high-risk patient cohorts from unified healthcare records. Random Forest classifiers with SHAP explainability, designed for clinical governance and early intervention planning at ICS or trust level.
Random ForestSHAPDatabricksAzure ML
[PROOF POINT]
"Achieved >85% weekly forecast accuracy for global travel organization support volume prediction — reducing overstaffing by 18% and improving SLA adherence by 22% through a fully automated XGBoost + Prophet ensemble with daily retraining on AWS SageMaker."
>85%
WEEKLY FORECAST ACCURACY
18%
OVERSTAFFING REDUCTION
22%
SLA ADHERENCE IMPROVEMENT
[ML OPERATIONS LIFECYCLE]
Build. Deploy. Monitor. Retrain.
Production ML at DataGravity is not a one-time model build — it is a continuous operational system. Every model we ship runs through a structured six-stage lifecycle with automated retraining triggered on drift detection, maintaining accuracy as the real world changes around it.
[ DATA SCIENCE ]
ML Operations Lifecycle
PRODUCTION ML PIPELINE — BUILD · DEPLOY · MONITOR · RETRAIN
01
Data Collection
Ingestion · Labelling · Quality
Airflow · Python · S3
→
02
Feature Engineering
Selection · Encoding · Scaling
Pandas · scikit-learn
→
03
Model Training
XGBoost · Prophet · LSTM · RF
SageMaker · Keras
→
04
Evaluation
SHAP · Metrics · Validation
MLflow · scikit-learn
→
05
Deployment
REST API · Batch Scoring
SageMaker · Flask · Lambda
→
06
Monitoring
Drift · Accuracy · Alerts
MLflow · CloudWatch
RETRAIN ON DRIFT
DATA → FEATURES → TRAINING
Build Phase
Data ingestion and quality checks · Feature engineering and selection · Model training with hyperparameter tuning · SHAP explainability validation before evaluation gate.
EVALUATION → DEPLOYMENT
Ship Phase
Hold-out validation against business KPI threshold · Containerised deployment via SageMaker or Flask API · A/B traffic splitting for production validation · Rollback-ready versioning.
MONITORING → RETRAIN
Sustain Phase
Weekly accuracy scoring vs baseline · Data drift detection on feature distributions · Automated retraining trigger when MAPE exceeds threshold · MLflow model registry for full auditability.
[TECHNOLOGY EXPERTISE]
The ML Stack.
ML FRAMEWORKS
Scikit-learnXGBoostLightGBMTensorFlow / KerasPyTorch
TIME SERIES & FORECASTING
Facebook ProphetLSTM (Keras)ARIMA / SARIMAN-BEATSStatsmodels
NLP
spaCyNLTKBERT / TransformersBERTopicGensim
MLOPS & DEPLOYMENT
AWS SageMakerMLflowDockerGitHub ActionsAWS LambdaSHAP
Ready to move from
notebooks to production?
Tell us about your prediction problem. We'll tell you what's technically feasible.