---
title: "Ankesh Kumar - Data Science & AI in Bengaluru | Tal"
description: "Meet Ankesh Kumar, Data @ Printify (FYUL) | Analytics Engineering • Experimentation & Causal Inference • Agentic AI - Claude Skills, Snowflake Intelligence | ex-Better.com | IISc | NIT Trichy based in Bengaluru. AI stack includes claude code, claude, and n8n. Explore public work links including git..."
image: "https://tal.club/meet/ankesh-kumar-1/share-image"
---

# Ankesh Kumar on Tal

## Headline

Data @ Printify \(FYUL\) \| Analytics Engineering • Experimentation & Causal Inference • Agentic AI \- Claude Skills, Snowflake Intelligence \| ex\-Better\.com \| IISc \| NIT Trichy

## About

I build data products and the tooling that makes data teams faster\. 8\+ years across fintech and e\-commerce — shipping production ML, owning company\-wide experimentation frameworks, and leading post\-merger data\-model consolidation at scale\. 🔹 Currently at Printify \(now FYUL, post Printify \+ Printful merger\) — sole developer of the Retail Price Recommendation system serving 270K\+ merchants, and led post\-merger unification of Printify and Printful data models \(reconciling divergent GMV definitions and designing the merged dimensional layer that powers C\-suite dashboards\)\. 🔹 Most excited right now about agentic AI for data teams\. Building Claude\-powered skills, plugins, and MCP servers that automate the drudge work analysts hate — SQL generation, Looker dashboard scaffolding, LookML linting, automated PR generation\. The piece I'm most proud of is a full Power BI → Looker migration agent that parses DAX, data models, and visuals and auto\-generates equivalent LookML views/explores/dashboards with validation checks — turning a months\-long manual rewrite into an automated pipeline\. 🔹 Earlier at Better Mortgage \(New York & Gurugram\) — operations \+ product analytics, regression models for loan funding cycle time, vendor performance frameworks\. Helped scale the India analytics team from 4 to 20\+\. 🔹 Represented Printify at the Data Innovation Summit 2023 in Stockholm\. Travelled to 15\+ countries; worked on the ground in India, the USA, and Latvia\. Reach out: ankeshkarn@gmail\.com Stack: SQL • Python • Snowflake • Looker/LookML • dbt • Airflow • Power BI • Claude Agent SDK • MCP • XGBoost • Docker/K8s

## Location

Bengaluru

## AI stack

claude code

claude

n8n

openai

claudecode

ChatGPT

## Work experience

Data Science & AI at FYUL, Dec 2022 \- Present · 3 yrs 9 mos, Riga, Latvia, • Agentic AI for data teams \(current focus\): Claude\-powered skills, plugins, and MCP servers that automate Looker dashboard generation, SQL authoring/formatting, automated PR creation, and LookML refactoring — cutting repetitive analyst work by 40–60% per workflow\. • Power BI → Looker migration \(end\-to\-end automation\): Designed and shipped an agentic pipeline that parses Power BI artifacts \(DAX, data models, visuals\) and auto\-generates equivalent LookML views, explores, and dashboards with validation checks — turning a months\-long migration into an automated pipeline\. • Snowflake Intelligence agents: Custom agents over Snowflake Intelligence \(semantic views \+ Cortex\) serving natural\-language analytics to non\-technical stakeholders\. • Retail Price Recommendation system \(sole developer\): End\-to\-end production price\-rec engine serving margin\-safe low/median/high prices to 270K\+ merchants\. Hierarchical fallback logic \(group → blueprint → variant with branding/channel drop\-downs\), margin\-guard, psychological rounding; deployed via Docker \+ Jenkins \+ Helm on Kubernetes\. • Post\-merger data\-model consolidation \(Printify \+ Printful → FYUL\): Led unification of two independent data ecosystems — reconciled divergent GMV/revenue definitions, mapped overlapping entities \(orders, merchants, catalogs\), and designed the merged dimensional layer powering C\-suite KPI dashboards\. • Experimentation leadership: Drove company\-wide democratization of A/B testing; enabled 5\+ cross\-functional teams to run experiments independently, reducing reliance on central analytics by \~60%\. • Causal inference: Synthetic control \+ DiD evaluation for a cashback campaign after control contamination; informed $M\-level marketing allocation\. • Survival analysis: Kaplan\-Meier framework for enterprise distributor and store activation funnels\. • Production ML: Order\-processing\-time estimation for fulfilment routing; co\-owned the Order Routing production\-speed model \(XGBoost\)\.

Analytics Strategy at Better, Apr 2022 \- Dec 2022 · 9 mos, New York City Metropolitan Area, • Owned analytics for volume\-management framework redesign to improve unit economics\. • Reported business and initiative metrics to VP/SVP operations leadership in weekly executive reviews\. • Built regression models explaining drivers of loan funding cycle time \(R² = 0\.82\)\. • Prototyped cost\-prediction models for property valuation\. • Developed API integrations across internal/external systems for automated data pipelines\.

Senior Data Analyst at Better, Apr 2021 \- Apr 2022 · 1 yr 1 mo, Gurugram, Haryana, India, • Partnered with Product and Ops on interventions that reduced loan funding cycle time by \~10 days\. • Built monitoring workflows and dashboards that reduced operational backlogs by \~50%\. • Designed vendor performance metrics supporting expansion to 3 additional appraisal vendors\. • Built performance dashboards tracking productivity, efficiency, and quality for 3,000\+ contributors\.

Data Analyst 2 at Better, Oct 2020 \- Apr 2021 · 7 mos, Gurugram, Haryana, India, • Introduced performance dashboards across 6 appraisal vendors\. • Automated 100\+ SQL workflows in Metabase/Looker\. • Interviewed 50\+ candidates; delivered SQL/Looker/Tableau training; helped scale India analytics team from 4 to 20\+\.

Analyst at LatentView Analytics, Jul 2018 \- Nov 2019 · 1 yr 5 mos, Greater Chennai Area, • Lead\-generation recommendation engine using RFM scoring and similarity indexing — improved campaign engagement by 6% and drove £5M in additional investments for a financial\-services client\. • Churn / disinvestment prediction classification model using investor interactions, transactions, consultant ratings, and product performance features\. • Power BI dashboard tracking money flows \(top\-ups, cross\-sells, withdrawals\) for executive KPI reporting\.

Campaign Coordinator at Coalition of Leaders for Sustainable Development, Jul 2017 \- Dec 2017 · 6 mos, India

Marketing Manager at Prodigy NIT Trichy, Nov 2016 \- Sep 2017 · 11 mos, NIT Trichy, Prodigy is an annual technical symposium of the Production engineering department\. As a marketing manager, I will be working in a team to bring down sponsors for Prodigy'17\.

## Education

Bachelor’s Degree, Production Engineering, National Institute of Technology, Tiruchirappalli

Postgraduate Degree, Advanced Deep Learning, Indian Institute of Science \(IISc\)

## Profile highlights

what i've built with AI: 1\. 0\-1 Agentic Analytics Stack 2\. AI experimentation Platform 3\. Proactive BI Listening Agent 4\. Synergies AI

I'm the person in every team who…: gets most of the complex problem statements

My hot take about working with AI is…: so much fun experimenting

My biggest achievement in college was…: Contributing to development of bullet proof jackets for Indian Army

My proudest non\-work achievement: Hopping by the Bangalore lakes\!

The role I'd take a 20% pay cut for: AI engineer

## Public links

- github: https://github\.com/ankumar07
- medium: https://medium\.com/@ankeshkarn

[View the original profile](https://tal.club/meet/ankesh-kumar-1)

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