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Senior Data Scientist
Locations: Bangalore | Mumbai | Gurgaon | Pune | Chennai
Experience: 5.5–10 Years
Compensation: Up to ₹36 LPA
Employment Type: Full-Time
Role Overview
We are looking for experienced Data Scientists to join an Artificial Intelligence and Machine Learning team working on advanced analytics, statistical modelling, and machine learning solutions.
The role involves solving complex business problems using sophisticated AI/ML techniques, conducting independent research and experimentation, and delivering scalable, high-quality data science solutions.
Key Responsibilities
Required Qualifications
Preferred Qualifications
Experience in one or more of the following areas will be an advantage:
Experience in any of the following domains is preferred:
Additional experience with:
Education
B.E./B.Tech/M.Tech in Computer Science, Engineering, Data Science, Mathematics, Statistics, or a related technical discipline, or equivalent experience.
*Note: Candidates with strong hands-on Data Science and Machine Learning experience are encouraged to apply.
Job ID: 153429273
Skills:
data engineering , Machine Learning, Sql, Deep Learning, Git, Big Data Technologies, Agile, Databricks, Rest Apis, Python, Data Transformation, GenAI, scikit-learn, MLlib, NoSQL Databases
Skills:
snowflake , Sql, Tensorflow, Pandas, Pytorch, Spark, Python, Scikit-learn, vector databases, MLflow, prompt engineering, Agentic AI systems, MLOps practices, MCP Model Context Protocol, LLM-powered applications, Kubeflow, Generative AI LLM Proficiency, RAG pipelines
Skills:
Tensorflow, System Design, Computer Vision, Python, Pyspark, Torch, Recommendation engines, Graph Knowledge Bases, Large-scale Search systems, Image Understanding models, Generative AI
Skills:
data engineering , Machine Learning, Python, Sql, Data Analysis, Causal AI, Structured Causal Models
Skills:
Databricks, Sql, Tensorflow, Deep Learning, Machine Learning, Pytorch, Data Science, Gcp, Sparql, AWS, Python, Azure, cloud environments, semantic data systems, scikit-learn, GQL, semantic models, embeddings, enterprise ontologies, GenAI systems, RAG, semantic retrieval, Statistical Modeling, Cypher, applied AI, enterprise knowledge grounding, graph query languages, ML libraries, vector databases, knowledge engineering, big data infrastructure