About the job :

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, you'll 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 - 7 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 design.

- 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 You'll Need to Be Successful (Required Skills) :

- Large Language Model (LLM) Experience : At least 5 years of hands-on experience working with pretrained 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.

Preferred Skills :

- At least 1 year of experience working with healthcare data or in healthcare IT environments.

- Familiarity with electronic health record (EHR) systems and healthcare workflows.

- Understanding of healthcare data privacy regulations (HIPAA, HITECH).

- Knowledge of clinical data standards and interoperability frameworks.

- Knowledge of MLOps practices and model deployment pipelines.

- Familiarity with natural language processing for clinical text analysis.

- Experience with time series analysis for patient monitoring data.