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Most Data Engineering is focused on the problem of moving, storing, or retrieving data. 

Most Data Science is close to the business, applying domain knowledge and statistics to the right data sets to create eureka moments or little crystal balls.


It’s this void between data engineering and data science, where the raw data needs to be nimbly, scalably prepped for every data science need, from analytical to ML-predictive, that Scribble Data operates.

Our platform, Enrich, helps prep data at scale (feature engineering) for data science, and our consulting services are aimed at turning every data science team into well-oiled machines.


We Help You Understand Data Use

Understanding the datasets that are in production, in the context of the business problem that needs to be solved, and surfacing summaries to data scientists


We Implement Your Data Pipelines

Implementing pipelines to generate the continuously computed datasets that ML models need to be trained on, and fed with when put into production.

Dr. Venkata Pingali 

Co-Founder & CEO
Former VP-Analytics, FourthLion

Founder, eLuminos Energy Analytics

BTech (IIT) & PhD (USC)

Venkata is a student of the history of technology. He enjoys the process of dismantling the hurdles to using data, in ways that are robust and defensible. In ways that create certainty and trust.


Venkata did his bachelor’s in computer science from IIT Bombay, and his PhD. from USC.

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Indrayudh Ghoshal 

Co-Founder & CBO
Former COO, FourthLion

Business Intelligence, CIBC

BEng (McGill) & MBA (UToronto)

Indra loves learning about how people interact with technology. How they adopt and when they discard - the little niggles when they hit technology bumps that should've been ironed out, the half-smiles when something is easier or works better than expected. Indra did his bachelor’s in electrical engineering from McGill U. and his MBA from UToronto.

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So much of our decision making happens in uncertainty.

Historically, we’ve figured that data would help us combat this uncertainty, to help us make more accurate predictions and better decisions. But the reality is that data is being generated indiscriminately. We are now grappling with all the noise that data causes - our continuous ‘data exhaust’ simply causes ‘data pollution’.

In this context, as businesses lean on data for their machine learning to make better decisions, there are opportunities, and dragons on this path. Few things excite us as much as our journey down this path.


2074 (#17) 16 D Main,

H.A.L 2nd Stage,


Bangalore 560008

CIN: U72900KA2016PTC097462

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