MODULE LEAD - Machine Learning

Happiest Minds Technologies; Maharashtra; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Happiest Minds Technologies is seeking a Senior Quantitative Scientist to analyze health data and deploy machine learning solutions. The role includes developing algorithms for clinical data, collaborating with cross-functional teams, and leveraging advanced natural language processing techniques for insights.

The ideal candidate will have over 5 years of experience in health data analytics, strong programming skills in Python and SQL, and familiarity with clinical datasets. Join us to contribute to impactful healthcare applications.

Qualifications

• 5+ years of hands-on experience with electronic health records and analytical methodologies.
• Strong experience in machine learning, LLM implementation & deployment.
• Familiarity with clinical datasets and coding systems such as ICD, CPT, and RxNorm.

Responsibilities

• Develop state-of-the-art natural language processing techniques for clinical data.
• Drive research on language modeling focused on scientific accuracy.
• Collaborate with cross-functional teams to translate clinical questions into analytics.

Skills

Health data analytics

Machine learning

Natural language processing (NLP)

Python programming

Pyspark

SQL

Tools

Databricks

Amazon Sagemaker

Visual Studio Code

Job description

Job Description:- Looking for a Senior Quantitative Scientist with a background in health data analytics and experience leveraging LLM. Develop and deploy machine learning and LLM-based solutions to analyze structured and unstructured clinical data, including electronic health records. Responsible for building algorithms, conducting data analysis, and generating insights to support real-world healthcare applications. Collaborate with cross‑functional teams to translate clinical requirements into scalable data science solutions while ensuring model accuracy, explainability, and continuous improvement.

Responsibilities

• Develop and leverage state‑of‑the‑art advances in natural language processing using pre‑trained LLMs for analyzing and reasoning over clinical notes and other unstructured data in the context of clinical problems.
• Drive cutting‑edge research on language modeling with emphasis on scientific accuracy and explainability.
• Communicate analysis results via presentations to a multi‑disciplinary audience using clear, intuitive visualizations.
• Establish and maintain best practices for data exploration, end‑to‑end model development and deployment lifecycle, and data/code/documentation management.
• Work on Qdata development to enable commercial projects leveraging real‑world data through responsibilities such as creation of study plans, implementation of analyses, development of algorithms, and/or writing of publications.
• Collaborate cross‑functionally with teams (e.g., Commercial, Product, Medical, Engineering/Technology, etc.) to translate clinical investigation questions into detailed data analytics requirements for internal and external projects.
• Provide mentorship and knowledge sharing to team members in standardizing machine learning/natural language processing best practices.

Basic Requirements

• 5+ years of hands‑on experience with messy data (e.g., electronic health records, outcomes data) and analytical methodologies.
• Strong experience on machine learning, LLM implementation & deployment, machine learning, NLP, AI.
• Must have worked on AI platforms for cross functional adoption.
• Strong familiarity with programming languages, especially Python, Pyspark, SQL.
• Strong familiarity with coding platforms, especially Databricks, Amazon Sagemaker, Visual Studio Code.
• Strong familiarity with unstructured text processing techniques.
• Familiarity with clinical datasets and coding systems such as ICD, CPT, and RxNorm.
• Ability to work effectively with cross‑functional teams.
• Clear communication skills and able to deliver internal/external presentations.
• Ability to prioritize and manage multiple projects with high attention to detail.