Back to jobs

Data Scientist

Reads like a ML Engineer role: putting models into production.

Pay
Pay not listed
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Develop, evaluate, and deploy ML, deep learning and LLM models

  • Own the end-to-end data science lifecycle, from problem definition to monitoring

  • Build reproducible, well-documented model workflows and pipelines

  • Implement model QA/QC with validation, drift detection, and experiment logs

  • Collaborate with product and domain teams to shape modeling strategies

From the employer’s posting
Develop, evaluate, and deploy ML, deep learning & LLM models
Own end-to-end DS lifecycle: problem definition → deployment → monitoring
Build reproducible, well-documented model workflows & pipelines
Implement strong model QA/QC: validation, drift detection, experiment logs
Collaborate with product & domain teams to shape modeling strategies

Tools in this posting

  • Python
  • MLflow
  • NumPy
  • pandas
  • PyTorch
  • scikit-learn
  • TensorFlow
Source — Tool mentions in context
Data Scientist (3-6 years of experience - Remote/Hybrid Bengaluru) Design, build, and deploy ML and LLM-based systems that drive personalization, assist teachers, and improve programs. What you’ll do Develop, evaluate, and deploy ML, deep learning & LLM models Own end-to-end DS lifecycle: problem definition → deployment → monitoring Build reproducible, well-documented model workflows & pipelines Implement strong model QA/QC: validation, drift detection, experiment logs Collaborate with product & domain teams to shape modeling strategies Continuously improve performance, documentation, reliability & processes What we’re looking for 3–6 years in applied ML / ML engineering / NLP / LLM development Strong fundamentals: statistics, ML algorithms, deep learning Python (pandas, numpy, scikit-learn, PyTorch/TensorFlow) ML workflow tools (MLflow, experiment tracking, registries) Experience with LLMs, fine-tuning, embeddings, vector stores, RAG Experience deploying models through APIs or containerized workflows Key Success Factors High ownership, clean engineering, excellent documentation Excellent communication and collaboration across teams to translate needs into solutions Curiosity, fundamentals, and bias for shipping reliable systems

Posting record

Report an error
Status in our records
Live
First seen by us
Aug 14, 202683 days ago
Recorded sightings
122
Last seen by us
Nov 6, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 83

Up longer than most. Similar jobs usually come down between day 6 and day 70.

From 4,523 similar postings that came down. Coming down is not always a hire.

Your next step

Check the employer’s posting for the current role and application details.

Already applied? Track this application

Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 625, the most 1,094, and the latest week had 876.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Data Scientist (3-6 years of experience - Remote/Hybrid Bengaluru) Design, build, and deploy ML and LLM-based systems that drive personalization, assist teachers, and improve programs. What you’ll do Develop, evaluate, and deploy ML, deep learning & LLM models Own end-to-end DS lifecycle: problem definition → deployment → monitoring Build reproducible, well-documented model workflows & pipelines Implement strong model QA/QC: validation, drift detection, experiment logs Collaborate with product & domain teams to shape modeling strategies Continuously improve performance, documentation, reliability & processes What we’re looking for 3–6 years in applied ML / ML engineering / NLP / LLM development Strong fundamentals: statistics, ML algorithms, deep learning Python (pandas, numpy, scikit-learn, PyTorch/TensorFlow) ML workflow tools (MLflow, experiment tracking, registries) Experience with LLMs, fine-tuning, embeddings, vector stores, RAG Experience deploying models through APIs or containerized workflows Key Success Factors High ownership, clean engineering, excellent documentation Excellent communication and collaboration across teams to translate needs into solutions Curiosity, fundamentals, and bias for shipping reliable systems
Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

Location not supplied.

This passage needs a closer read in the full description.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

AI answers unavailable

We couldn’t identify enough role detail in this saved description to support an AI answer. Read the full posting