Senior Data Scientist

Job Summary:

We are looking for a Senior Data Scientist who is passionate about translating cutting-edge machine-learning and GenAI research into deployable solutions across all HiLabs products. You will lead experimentation, model development, and analytical storytelling that power our healthcare data quality platform—partnering closely with engineers, product managers, and MLOps to drive measurable impact.

Key Responsibilities:

Applied AI & Machine Learning

• Research, prototype, and refine classical ML, deep-learning, and GenAI/LLM models for data cleansing, entity resolution, and knowledge extraction.
• Design and run rigorous offline/online experiments; define success metrics, analyze results, and iterate quickly.
• Develop RAG pipelines, prompt-engineering strategies, and evaluation frameworks for LLM-based features.

Data Science & Analytics

• Mine large, messy healthcare datasets to discover insights, quantify data quality issues, and prioritize model improvements.
• Build interpretable models and visualizations that communicate value to clinicians and business stakeholders.
• Champion statistical best practices, bias detection, and fairness auditing in all modeling workflows.

Collaboration & Influence

• Partner with product managers to translate ambiguous problems into clear ML objectives and roadmaps.
• Work hand-in-hand with Machine Learning Engineers (MLEs) to productionize your models and validate performance in the wild.
• Mentor junior data scientists; share knowledge through brown-bags, code reviews, and internal playbooks.

Thought Leadership

• Track state-of-the-art research in NLP, LLM alignment, and representation learning; assess applicability to HiLabs use cases.
• Publish internal whitepapers, contribute to conference submissions, and represent HiLabs in external forums when appropriate.

Desired Profile:

• Education: Bachelor’s or Master’s in Computer Science, Statistics, Mathematics, or related quantitative field; PhD is a plus.
• Experience: 5–7 years applying ML/NLP techniques to real-world problems, ideally in healthcare or other regulated domains.

Core Skills:

• Strong Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow) and SQL for feature engineering and analysis.
• Practical knowledge of LLM fine-tuning, prompt engineering, vector search (FAISS/Pinecone/OpenSearch), and evaluation.
• Deep understanding of statistical inference, causal analysis, and experiment design (A/B, multivariate).
• Familiarity with data-science workflow tools (Jupyter, MLflow, Weights & Biases) and Git-based collaboration.
• Exposure to cloud ML services (AWS SageMaker, Bedrock) is a plus but not mandatory.

Soft Skills:

• Excellent communication—able to simplify complex technical concepts for non-technical audiences.
• Curious, impact-oriented mindset with a bias for rapid iteration and evidence-based decision-making.
• Proven ability to thrive in cross-functional, fast-moving teams and to mentor peers.