Data Science

Algorithmic approaches to extract patterns, insights, and value from data, in a business decision-making context. The end-to-end pipeline of Data > Modeling > Decisioning > Deployment, when integrated and applied in specific business decision context, can deliver clear and measurable impact both on revenue and cost. Our objective in delivering data science is just that – ensuring measurable business value.

Predictive Analytics

Leverage structured and unstructured data to predict future events – e.g. trends, pricing decisions, fraud risk, customer attrition etc.

Machine Learning

New age algorithms to derive value from complex and big data – e.g. real-time bidding, IoT sensor data based maintenance, recommender systems. Text Analytics and NLP Draw meaningful insights from text data – e.g. conversation themes, topics, sentiment, sales leads, customer satisfaction etc.


Achieve optimal revenue, margin or cost in a given decision framework – e.g. maximize marketing reach, resource optimization, inventory management.


Drive superior operations and planning through demand forecasting, cost planning, granular sales, shipment forecasting etc.

Our Approach

Most data science problems can be divided into three sequential phases – problem definition & data discovery, model estimation & validation, insights & business application. We have broad frameworks to systematically approach a wide variety of data science problems to ensure business value


Problem Definition
Data Preparation
Data Discovery


Feature Engineering
Model Development
Model Validation


Insights & Inferences
KPI Dashboards
Production & Monitoring