Senior Data Scientist

Job Title : Senior Data Scientist

Experience: 5 to 12 Years

Work Mode: Hybrid

Location : Pune

Notice Period: Immediate joiners preferred ( 0 to 30 days)

We are seeking a highly skilled Senior Data Scientist with strong expertise in Machine Learning, Recommender Systems, and Generative AI (LLM/LVM). The ideal candidate will have hands-on experience developing, deploying, and scaling enterprise-grade AI/ML solutions and working closely with Data Engineering, ML Engineering, and business stakeholders.

The role involves building end-to-end machine learning solutions, recommendation engines, and GenAI applications while ensuring scalability, reliability, monitoring, and responsible AI practices.

Key Responsibilities

Data Science & Advanced Analytics

• Develop and deploy end-to-end ML models from ideation through production.
• Perform EDA, feature engineering, predictive modeling, and model evaluation.
• Build predictive and prescriptive models using statistical and machine learning techniques.
• Optimize models for accuracy, performance, scalability, and business impact.

Machine Learning Services

• Design and implement scalable ML pipelines for training, testing, validation, and deployment.
• Work with cloud ML platforms such as Azure ML, AWS SageMaker, and Google Vertex AI.
• Implement model lifecycle management, including versioning, monitoring, retraining, and governance.
• Develop production-ready ML services and APIs.

Recommender Systems

• Design and develop collaborative, content-based, and hybrid recommendation systems.
• Build ranking and personalization solutions using large-scale datasets.
• Implement user segmentation and recommendation strategies.
• Evaluate recommendation models using metrics such as Precision@K, Recall@K, and NDCG.

Generative AI – LLM & LVM

• Build and deploy LLM-powered applications, including chatbots, copilots, document intelligence, and intelligent assistants.
• Design and implement RAG (Retrieval-Augmented Generation) architectures.
• Work with models and platforms such as OpenAI, Azure OpenAI, Llama, and Hugging Face.
• Develop solutions for text generation, summarization, classification, and multimodal/image/video understanding.
• Implement prompt engineering and optimize prompts for production use cases.
• Work with frameworks such as LangChain and LlamaIndex.

Data Engineering Collaboration

• Define data requirements and collaborate with Data Engineering teams on data pipeline design.
• Ensure data quality, availability, governance, and reliability.
• Work with Apache Spark, Databricks, Hadoop, ETL pipelines, and data warehousing technologies.

ML Engineering & Production Deployment

• Deploy ML and AI models through APIs and microservices.
• Containerize applications using Docker and Kubernetes.
• Integrate ML solutions into enterprise CI/CD pipelines.
• Collaborate with ML Engineering and DevOps teams to support production deployments.

Model Monitoring & Responsible AI

• Monitor model performance, drift, degradation, and reliability.
• Implement logging, alerting, and model monitoring mechanisms.
• Apply explainability and interpretability techniques.
• Ensure responsible AI practices covering fairness, transparency, governance, and bias mitigation.

Required Qualifications & Experience

• 5–8 years of experience in Data Science, Machine Learning, Artificial Intelligence, or a related field.
• Proven experience in end-to-end ML model development, deployment, and production support.
• Strong hands-on experience with Python and modern ML frameworks.
• Experience with at least one cloud ML platform:
• Azure ML
• AWS SageMaker
• Google Vertex AI
• Strong experience developing Generative AI/LLM solutions.
• Hands-on experience with RAG, prompt engineering, and LLM frameworks.
• Experience collaborating with Data Engineering, ML Engineering, and business teams.
• Experience delivering enterprise-scale AI/ML applications using Agile methodologies.