Senior Data Scientist

Looking for Top Talent in Data Science healthcare Domain

Immediate Joiner/30 Days

Work From Office - 5 Days (Hebbal Location)

Job Summary

We are seeking a Senior Data Scientist Gen AI with hands-on experience in building and deploying data-driven solutions. You will work closely with cross-functional teams to extract insights, develop machine learning models, and deploy scalable analytics solutions across various cloud platforms. As a Senior Data Scientist Gen AI, youll play a key role in harnessing data to drive better outcomes, improve performance, and enhance the lives of those served by our clients.

Key Responsibilities:

• The ability to design and develop ML, Gen AI, NLP, LLM Models for AI data pipelines. Model components will include data ingestion, preprocessing, Retrieval Augmented Generation (RAG), NLP/LLM model development, fine-tuning and prompt engineering .
• Analyze large, complex healthcare datasets including electronic health records (EHR) and claims data
• Develop statistical models for patient risk stratification, treatment optimization, population health management, and revenue cycle optimization
• Build models for clinical decision support, patient outcome prediction, care quality improvement, and revenue cycle optimization
• Create and maintain automated data pipelines for real-time analytics and reporting
• Work with healthcare data standards (HL7 FHIR, ICD-10, CPT, SNOMED CT) and ensure regulatory compliance
• Develop and deploy models in cloud environments while creating visualizations for stakeholders
• Present findings and recommendations to cross-functional teams including clinicians, product managers, and executives

Qualifications required:

• Bachelor's degree in data science, Statistics, Computer Science, Mathematics, or related quantitative field
• 5-8 years of hands-on experience in data science, analytics, or machine learning roles
• Knowledge of ML Ops practices deploying and operationalize AI models.
• Familiarity with cloud platforms (Azure, AWS, GCP) and containerized deployments (Docker, Kubernetes).
• Demonstrated experience working with large datasets and statistical modeling
• Proficiency in Python or R for data analysis and machine learning
• Experience with SQL and database management systems
• Knowledge of machine learning frameworks such as scikit-learn, TensorFlow, PyTorch
• Familiarity with data visualization tools such as Tableau, Power BI, matplotlib, ggplot2
• Experience with version control systems (Git) and collaborative development practices
• Strong foundation in statistics, hypothesis testing, and experimental designJD Template for Senior Data Scientist Gen AI
• Experience with supervised and unsupervised learning techniques
• Knowledge of data preprocessing, feature engineering, and model validation
• Understanding of A/B testing and causal inference methods
• Experience with cloud platforms and big data technologies such as Spark, Hadoop

What Youll Need to Be Successful (Required Skills):

• Large Language Model (LLM) Experience:

  At least 5 years of hands-on experience working with pre-trained language models (GPT, BERT, T5) including fine-tuning, prompt engineering, and model evaluation techniques. LLM experience should include Claude, Llama models.Understanding of RAG techniques is a critical skill.

• Generative AI Frameworks:

  Proficiency with generative AI libraries and frameworks such as Hugging Face Transformers, Lang Chain, OpenAI API, or similar platforms for building and deploying AI applications

• Prompt Engineering and Optimization:

  Experience designing, testing, and optimizing prompts for various use cases including text generation, summarization, classification, and conversational AI applications

• Vector Databases and Embeddings:

  Knowledge of vector similarity search, embedding models, and vector databases (Pinecone, We aviate, Chroma) for building retrieval-augmented generation (RAG) systems

• AI Model Evaluation:

  Experience with evaluation methodologies for generative models including BLEU scores, ROUGE metrics, human evaluation frameworks, and bias detection techniques

• Multi-modal AI Systems:

  Familiarity with multi-modal generative models combining text, images, and other data types, including experience with vision-language models and cross-modal applications

• AI Safety and Alignment:

  Understanding of responsible AI practices including content filtering, bias mitigation, hallucination detection, and techniques for ensuring AI outputs align with business requirements and ethical guidelines