Data Scientist

Public Listed - Product Based company

Data Scientist

Recruiting Bond

Company

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Data Scientist

at Public Listed - Product Based company

Agency job

via Recruiting Bond

4 - 8 yrs

₹25L - ₹70L / yr

Bengaluru (Bangalore)

Skills

Data Science

data platforms

Data-flow analysis

Data pipelines

AI Infrastructure

Large Language Models (LLM)

Large Language Models (LLM) tuning

Agentic Systems

AI Agents

Machine Learning (ML)

Deep Learning

Natural Language Processing (NLP)

Statistical Modeling

Gen AI

Retrieval Augmented Generation (RAG)

Data Warehouse (DWH)

Data modeling

Python

PyTorch

AI/ML

Multi-modal AI

SQL

PostgreSQL

MongoDB

Query planning

Prompt engineering

ETL

ELT

AI copilots

Generative AI

Artificial Intelligence (AI)

Google Vertex AI

Vector database

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

Legacy systems → AI-native architectures
Static pipelines → autonomous, self-healing systems
Data platforms → intelligent, learning systems
Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

Data platforms optimize themselves using ML/LLMs
Pipelines are autonomous, self-healing, and adaptive
Queries are generated, optimized, and executed intelligently
Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

Build LLM-powered interfaces:
Natural language → SQL / pipelines / transformations
Design semantic data layers:
Embeddings, vector search, knowledge graphs
Develop AI copilots:
For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

Build self-healing ETL/ELT systems using AI agents
Create pipelines that:
Detect anomalies in real time
Automatically debug failures
Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

Apply ML/LLMs to:
Query planning and execution
Cost-based optimization using learned models
Workload prediction and scheduling
Build systems that:
Learn from query patterns
Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

Architect systems operating at:
Billions of events per day
Petabyte-scale data
Work with:
Distributed compute engines (Spark / Flink / Ray class systems)
Streaming systems (Kafka-class infra)
Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

Build closed-loop AI systems:
Execution → feedback → model updates
Develop:
Continual learning pipelines
Online learning systems for infra optimization
Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

Build agents that understand data systems
Enable:
Autonomous pipeline debugging
Root cause analysis for infra failures
Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

Strong grounding in:
Machine Learning, Deep Learning, NLP
Statistics, optimization, probabilistic systems
Distributed systems fundamentals
Deep understanding of:
Transformer architectures
Modern LLM ecosystems

Hands-On Expertise

Experience building:
LLM / GenAI systems (RAG, fine-tuning, embeddings)
Data platforms (warehouse, lake, lakehouse architectures)
Distributed pipelines and compute systems
Strong programming skills:
Python (ML/AI stack)
SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

Built or worked on:
Large-scale data pipelines
High-throughput distributed systems
Low-latency, high-concurrency architectures
Understand:
Query optimization and execution
Data partitioning, indexing, caching
Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

Built AI-powered data platforms or infra systems in production
Designed or contributed to:
Query engines / optimizers
Data observability / lineage systems
AI-driven infra or AIOps platforms
Experience with:
Multi-modal AI (logs, metrics, traces, text)
Agentic AI systems
Autonomous infrastructure
Worked on systems at scale comparable to:
Google (BigQuery-like systems)
Meta (real-time analytics infra)
Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

Data infrastructure or platform engineering teams
AI-first startups or research-driven environments
High-scale product companies

Experience building:

Internal platforms used by 1000s of engineers
Systems serving millions of users / high throughput workloads
Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

Can LLMs replace traditional query optimizers?
How do we build self-healing data pipelines at scale?
Can data systems learn from every query and improve automatically?
How do we embed reasoning and planning into infrastructure layers?
What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

Leading engineering schools in India and globally
Engineers with experience in top product companies, AI startups, or research-driven environments
That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

Legacy systems → AI-native architectures
Static pipelines → autonomous, self-healing systems
Data platforms → intelligent, learning systems
Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

Data platforms optimize themselves using ML/LLMs
Pipelines are autonomous, self-healing, and adaptive
Queries are generated, optimized, and executed intelligently
Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

Build LLM-powered interfaces:
Natural language → SQL / pipelines / transformations
Design semantic data layers:
Embeddings, vector search, knowledge graphs
Develop AI copilots:
For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

Build self-healing ETL/ELT systems using AI agents
Create pipelines that:
Detect anomalies in real time
Automatically debug failures
Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

Apply ML/LLMs to:
Query planning and execution
Cost-based optimization using learned models
Workload prediction and scheduling
Build systems that:
Learn from query patterns
Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

Architect systems operating at:
Billions of events per day
Petabyte-scale data
Work with:
Distributed compute engines (Spark / Flink / Ray class systems)
Streaming systems (Kafka-class infra)
Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

Build closed-loop AI systems:
Execution → feedback → model updates
Develop:
Continual learning pipelines
Online learning systems for infra optimization
Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

Build agents that understand data systems
Enable:
Autonomous pipeline debugging
Root cause analysis for infra failures
Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

Strong grounding in:
Machine Learning, Deep Learning, NLP
Statistics, optimization, probabilistic systems
Distributed systems fundamentals
Deep understanding of:
Transformer architectures
Modern LLM ecosystems

Hands-On Expertise

Experience building:
LLM / GenAI systems (RAG, fine-tuning, embeddings)
Data platforms (warehouse, lake, lakehouse architectures)
Distributed pipelines and compute systems
Strong programming skills:
Python (ML/AI stack)
SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

Built or worked on:
Large-scale data pipelines
High-throughput distributed systems
Low-latency, high-concurrency architectures
Understand:
Query optimization and execution
Data partitioning, indexing, caching
Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

Built AI-powered data platforms or infra systems in production
Designed or contributed to:
Query engines / optimizers
Data observability / lineage systems
AI-driven infra or AIOps platforms
Experience with:
Multi-modal AI (logs, metrics, traces, text)
Agentic AI systems
Autonomous infrastructure
Worked on systems at scale comparable to:
Google (BigQuery-like systems)
Meta (real-time analytics infra)
Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

Data infrastructure or platform engineering teams
AI-first startups or research-driven environments
High-scale product companies

Experience building:

Internal platforms used by 1000s of engineers
Systems serving millions of users / high throughput workloads
Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

Can LLMs replace traditional query optimizers?
How do we build self-healing data pipelines at scale?
Can data systems learn from every query and improve automatically?
How do we embed reasoning and planning into infrastructure layers?
What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

Leading engineering schools in India and globally
Engineers with experience in top product companies, AI startups, or research-driven environments
That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

Users love Cutshort

Read about what our users have to say about finding their next opportunity on Cutshort.

Shubham Vishwakarma

Full Stack Developer - Averlon

I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.

Companies hiring on Cutshort

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Digital Marketing Manager

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1 recruiter

Posted by Deepali Khadye

Remote only

4 - 8 yrs

₹10L - ₹24L / yr

Marketing Strategy

Digital Marketing

Partnership and Alliances

Affiliate marketing

Search Engine Optimization (SEO)

+9 more

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Strong execution discipline: balancing speed, quality, and persistence.

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Experience in the WordPress, SaaS, or website builder ecosystem.
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Past leadership in affiliate/influencer partnerships.
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KPI's for Success:

Increased traffic and conversions from owned channels.
Growth in signups, website traffic and product revenue.
Improved conversion rates across funnels and campaigns.
Number and quality of partnerships, affiliates, and creators engaged.
Execution of successful product launches or large campaigns.
Improved team velocity and on-time delivery of campaigns.

Looks interesting? Apply now and be part of our awesome team!

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Why You’ll Love Working With Us

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Marketing Strategy & Campaigns:

Own and execute data-driven marketing campaigns across SEO, paid ads, content, affiliates, and email.
Align channel strategies with business and product-level goals.
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Take ownership of channels like SEO, email, paid, content, or affiliates and ensure measurable performance improvements.
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Increased traffic and conversions from owned channels.
Growth in signups, website traffic and product revenue.
Improved conversion rates across funnels and campaigns.
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Data Analytics - Datorama

My Client is the world’s largest media investment company.

Agency job

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Gurugram

5 - 10 yrs

Best in industry

Data Analytics

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· Manage extraction of data sets from multiple marketing/database platforms and perform hygiene and quality control steps, either via Datorama or in partnership with Neo Technology team. Data sources will include: web analytic tools, media analytics, customer databases, social listening tools, search tools, syndicated data, research & survey tools, etc…

· Implement and manage data system architecture

· Audit and manage data taxonomy/classifications from multiple systems and partners

· Manage the extraction, loading, and transformation of multiple data sets

· Cleanse all data and metrics; perform override updates where necessary

· Execute all business rules according to requirements · Identify and implement opportunities for efficiency throughout the process

· Manage and execute thorough QA process and ensure quality and accuracy of all data and reporting deliverables

· Manipulate and analyze “big” data sets synthesized from a variety of sources, including media platforms and marketing automation tools

· Generate and manage all data visualizations and ensure data is presented accurately and is visually pleasing

· Assist analytics team in running numerous insights reports as needed

· Help maintain a performance platform and provide insights and ongoing recommendations around.

Requirements :

· 5+ years’ experience in an analytics position working with large amounts of data

· Hands-on experience working with data visualization tools such as Datorama, Tableau, or PowerBI

· Additional desirable skills include tag management experience, application coding experience, statistics background

· Digital media experience background preferred, including knowledge of Doubleclick and web analytics tools

· Excellent communication skills

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