Data Scientist (Product company, Mumbai)

Matchmaking platform

Data Scientist (Product company, Mumbai)

Data Scientist (Product company, Mumbai) at Matchmaking platform · Mumbai · 2 - 5 years · ₹15L - ₹28L / yr · Posted 21 Jan 2026

Data Scientist (Product company, Mumbai)

at Matchmaking platform

Agency job

via Peak Hire Solutions

2 - 5 yrs

₹15L - ₹28L / yr

Mumbai

Skills

Data Science

Python

Natural Language Processing (NLP)

MySQL

Machine Learning (ML)

Artificial Intelligence (AI)

Algorithms

Logistic regression

Decision trees

Gradient boosting

RESTful APIs

CI/CD

Docker

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

SQL

Amazon Redshift

Looker

Tableau

Storytelling

Review Criteria

Strong Data Scientist/Machine Learnings/ AI Engineer Profile
2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
Preferred (Company) – Must be from product companies

Job Specific Criteria

CV Attachment is mandatory
What's your current company?
Which use cases you have hands on experience?
Are you ok for Mumbai location (if candidate is from outside Mumbai)?
Reason for change (if candidate has been in current company for less than 1 year)?
Reason for hike (if greater than 25%)?

Role & Responsibilities

Partner with Product to spot high-leverage ML opportunities tied to business metrics.
Wrangle large structured and unstructured datasets; build reliable features and data contracts.
Build and ship models to:
Enhance customer experiences and personalization
Boost revenue via pricing/discount optimization
Power user-to-user discovery and ranking (matchmaking at scale)
Detect and block fraud/risk in real time
Score conversion/churn/acceptance propensity for targeted actions
Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
Design and run A/B tests with guardrails.
Build monitoring for model/data drift and business KPIs

Ideal Candidate

2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
Proven, hands-on success in at least two (preferably 3–4) of the following:
Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
Fraud/risk detection (severe class imbalance, PR-AUC)
Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
Propensity models (payment/churn)
Programming: strong Python and SQL; solid git, Docker, CI/CD.
Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
ML breadth: recommender systems, NLP or user profiling, anomaly detection.
Communication: clear storytelling with data; can align stakeholders and drive decisions.

Review Criteria

Strong Data Scientist/Machine Learnings/ AI Engineer Profile
2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
Preferred (Company) – Must be from product companies

Job Specific Criteria

CV Attachment is mandatory
What's your current company?
Which use cases you have hands on experience?
Are you ok for Mumbai location (if candidate is from outside Mumbai)?
Reason for change (if candidate has been in current company for less than 1 year)?
Reason for hike (if greater than 25%)?

Role & Responsibilities

Partner with Product to spot high-leverage ML opportunities tied to business metrics.
Wrangle large structured and unstructured datasets; build reliable features and data contracts.
Build and ship models to:
Enhance customer experiences and personalization
Boost revenue via pricing/discount optimization
Power user-to-user discovery and ranking (matchmaking at scale)
Detect and block fraud/risk in real time
Score conversion/churn/acceptance propensity for targeted actions
Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
Design and run A/B tests with guardrails.
Build monitoring for model/data drift and business KPIs

Ideal Candidate

2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
Proven, hands-on success in at least two (preferably 3–4) of the following:
Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
Fraud/risk detection (severe class imbalance, PR-AUC)
Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
Propensity models (payment/churn)
Programming: strong Python and SQL; solid git, Docker, CI/CD.
Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
ML breadth: recommender systems, NLP or user profiling, anomaly detection.
Communication: clear storytelling with data; can align stakeholders and drive decisions.

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Design, build, and deploy scalable machine learning models into production systems.
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Proven expertise in deploying ML models into production systems.
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Solid understanding of query optimization, storage systems, and data pipelines.
Excellent problem-solving skills, with the ability to design scalable solutions.
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• Partner with stakeholders to frame problems and metrics

• Communicate results and drive decisions

• Iterate on models from business feedback

Mandatory Skills

• 4+ years applied machine learning

• Strong Python (Pandas, NumPy, scikit-learn)

• Classical ML and deep learning (TensorFlow/PyTorch)

• Solid statistics and experiment design

• SQL and data wrangling at scale

• Model deployment / MLOps exposure

Nice to Have: NLP or computer vision; cloud ML (SageMaker, Azure ML)

Key Responsibilities

• Build, train, and evaluate ML and deep-learning models

• Engineer features and prepare data at scale

• Deploy models and monitor production performance

• Partner with stakeholders to frame problems and metrics

• Communicate results and drive decisions

• Iterate on models from business feedback

Mandatory Skills

• 4+ years applied machine learning

• Strong Python (Pandas, NumPy, scikit-learn)

• Classical ML and deep learning (TensorFlow/PyTorch)

• Solid statistics and experiment design

• SQL and data wrangling at scale

• Model deployment / MLOps exposure

Nice to Have: NLP or computer vision; cloud ML (SageMaker, Azure ML)

Sr. Data Scientist - Product based - Perm Role

Hiring for Top Product based company

Agency job

via by Manasi Chavan

Pune

8 - 12 yrs

₹15L - ₹20L / yr

Data Science

Python

Natural Language Processing (NLP)

Cloud Computing

Data Visualization

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

Data Scientist - Machine Learning & MLOps

Posted by Sonal Agarwal

Remote only

3 - 8 yrs

₹20L - ₹35L / yr

MLFlow

MLOps

Fine-tuning LLMs

KEY RESPONSIBILITIES

End-to-End ML Development

• Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.

• Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.

• Validate model performance using appropriate statistical techniques and domain knowledge.

MLOps & Production Deployment

• Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.

• Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.

• Ensure model reliability, observability, and performance in live production environments.

Language Models & LLM Applications

• Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.

• Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.

• Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.

• Support exploratory work around LLM integration and prompt engineering for internal tooling.

Domain-Driven Analytics

• Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.

• Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.

• Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.

REQUIRED QUALIFICATIONS

Education

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.

Experience

• 2–4 years of hands-on experience in a data science or machine learning role.

• Demonstrable experience deploying ML models in production environments (not just prototyping).

Technical Skills

• Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).

• Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.

• Hands-on experience with BERT-family models and Hugging Face Transformers library.

• Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.

• Solid understanding of SQL and working with large structured/unstructured datasets.

• Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).

GOOD TO HAVE

• Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).

• Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.

• Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.

• Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).

• Contributions to open-source ML projects or published research.

THIS ROLE IS NOT FOR YOU IF…

• You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.

• Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.

KEY RESPONSIBILITIES

End-to-End ML Development

• Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.

• Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.

• Validate model performance using appropriate statistical techniques and domain knowledge.

MLOps & Production Deployment

• Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.

• Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.

• Ensure model reliability, observability, and performance in live production environments.

Language Models & LLM Applications

• Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.

• Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.

• Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.

• Support exploratory work around LLM integration and prompt engineering for internal tooling.

Domain-Driven Analytics

• Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.

• Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.

• Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.

REQUIRED QUALIFICATIONS

Education

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.

Experience

• 2–4 years of hands-on experience in a data science or machine learning role.

• Demonstrable experience deploying ML models in production environments (not just prototyping).

Technical Skills

• Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).

• Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.

• Hands-on experience with BERT-family models and Hugging Face Transformers library.

• Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.

• Solid understanding of SQL and working with large structured/unstructured datasets.

• Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).

GOOD TO HAVE

• Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).

• Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.

• Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.

• Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).

• Contributions to open-source ML projects or published research.

THIS ROLE IS NOT FOR YOU IF…

• You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.

• Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.

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