Data Scientist ML/AI Mumbai (Product Company)
Were seeking a hands-on Data Scientist or ML Engineer to build and deploy ML models that drive consumer internet products. Focus on recommendation systems NLP fraud detection pricing or propensity modeling in a fast-paced B2C environment.
Key Responsibilities
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Develop and productionize ML models end-to-end using Python including classical algorithms like linear/logistic regression decision trees and gradient boosting.
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Implement use cases in recommender systems (retrieval/ranking NDCG) fraud/risk detection (PR-AUC) pricing models (elasticity/demand) or propensity models (churn/payment).
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Apply NLP techniques for text generation classification embeddings similarity models user profiling and unstructured text feature extraction.
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Deploy models via APIs Docker CI/CD pipelines on AWS or GCP.
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Collaborate with stakeholders using SQL for data querying git for version control and tools like Redshift/BigQuery Looker/Tableau for insights.
Must-Have Requirements
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2 years as Data Scientist/ML Engineer with hands-on model building and 7-8 models shipped to production.
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Strong Python expertise for classical ML algorithms.
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Experience in 2 use cases: recommenders image data fraud/risk pricing propensity.
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NLP exposure: text gen/classification embeddings similarity user profiling.
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Productionizing ML via APIs/CI/CD/Docker on AWS/GCP.
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2-5 years in consumer internet/B2C products preferred.
Data Scientist ML/AI Mumbai (Product Company) Were seeking a hands-on Data Scientist or ML Engineer to build and deploy ML models that drive consumer internet products. Focus on recommendation systems NLP fraud detection pricing or propensity modeling in a fast-paced B2C environment. Key Respon...
Data Scientist ML/AI Mumbai (Product Company)
Were seeking a hands-on Data Scientist or ML Engineer to build and deploy ML models that drive consumer internet products. Focus on recommendation systems NLP fraud detection pricing or propensity modeling in a fast-paced B2C environment.
Key Responsibilities
-
Develop and productionize ML models end-to-end using Python including classical algorithms like linear/logistic regression decision trees and gradient boosting.
-
Implement use cases in recommender systems (retrieval/ranking NDCG) fraud/risk detection (PR-AUC) pricing models (elasticity/demand) or propensity models (churn/payment).
-
Apply NLP techniques for text generation classification embeddings similarity models user profiling and unstructured text feature extraction.
-
Deploy models via APIs Docker CI/CD pipelines on AWS or GCP.
-
Collaborate with stakeholders using SQL for data querying git for version control and tools like Redshift/BigQuery Looker/Tableau for insights.
Must-Have Requirements
-
2 years as Data Scientist/ML Engineer with hands-on model building and 7-8 models shipped to production.
-
Strong Python expertise for classical ML algorithms.
-
Experience in 2 use cases: recommenders image data fraud/risk pricing propensity.
-
NLP exposure: text gen/classification embeddings similarity user profiling.
-
Productionizing ML via APIs/CI/CD/Docker on AWS/GCP.
-
2-5 years in consumer internet/B2C products preferred.
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