Lead Data Scientist - Healthcare Domain Specialist

Job Description: Lead Data Scientist Healthcare Domain Specialist
Location: Bangalore
Employment Type: Full-Time
Industry: Healthcare/AI/Analytics
Domain Expertise: Predictive Analytics, Healthcare Data
As a key leader in our data science team, you will define strategy, lead projects, and collaborate with healthcare professionals, engineers, and product teams to deploy scalable AI solutions.
Key Responsibilities
AI Model Development: Design, develop, and optimize predictive models for elderly fall risk assessment using advanced machine learning (ML) and deep learning techniques.
Data Analysis: Work with healthcare-specific data (e.g., patient records, sensor data, clinical data) to uncover patterns and actionable insights.
Domain Expertise Application: Leverage healthcare domain knowledge to ensure accuracy, reliability, and ethical use of models in predicting fall risks.
Collaborate with Experts: Collaborate with clinicians, healthcare providers, and crossfunctional teams to align AI solutions with clinical workflows and patient care strategies.
Data Engineering: Develop robust ETL pipelines to preprocess and integrate healthcare data from multiple sources, ensuring data quality and compliance.
Evaluation & Optimization: Continuously evaluate model performance and refine algorithms to achieve high accuracy and generalizability.
Compliance & Ethics: Ensure compliance with healthcare data regulations such as HIPAA, GDPR, and implement best practices for data privacy and security.
Research & Innovation: Stay updated with the latest research in healthcare AI, predictive analytics, and elderly care solutions, integrating new techniques as applicable.
Team Management: Guide all team members in technical and domain-specific problemsolving, manage day to day task deliverables, evaluate individual s performance and coach.
Stakeholder Management: Present insights, models, and business impact assessments to senior leadership and healthcare stakeholders.
Required Skills & Qualifications
Education: Master 's or PhD in Data Science, Computer Science, Statistics, Bioinformatics, or a related field. A strong academic background in healthcare is preferred.
Experience:
o 8 - 11 years of experience in data science, with at least 2 years in the healthcare domain.
o Prior experience in leading AI projects in healthcare startups, hospitals, or MedTech companies.
o Ability to work in cross-functional teams.
o Ability to publish papers and research findings related to healthcare data science
Technical Expertise: o Proficiency in Python, R, or other programming languages used for ML and data analysis.
o Hands-on experience with ML/DL frameworks (e.g., TensorFlow, PyTorch, Scikitlearn).
o Experience with time-series data, wearable/sensor data, or IoT data integration is a plus.
o Strong knowledge of statistics, probability, and feature engineering.
o Familiarity with cloud platforms (AWS, Azure, GCP) and tools for scalable ML pipelines.
Healthcare Domain Knowledge:
o Understanding of geriatric healthcare challenges, fall risks, and predictive care strategies.
o Familiarity with Electronic Health Records (EHR), wearable devices, and sensor data.
o Knowledge of healthcare data compliance (e.g., HIPAA, GDPR).
Soft Skills:
o Strong analytical and problem-solving abilities.
o Excellent communication skills to present findings to non-technical stakeholders.
o A collaborative mindset to work with interdisciplinary teams.
Preferred Qualifications
Knowledge of biomechanics or human movement analysis.
Experience with explainable AI (XAI) and interpretable ML models.
What We Offer
Opportunity to work on cutting-edge healthcare AI solutions that make a meaningful impact on elderly lives.
Competitive salary and benefits package.
Flexible work environment, with options for hybrid work.
Opportunities for professional growth and leadership.
Collaborative and inclusive culture that values innovation and teamwork.
LEVEL OF EXPERTISE
Data Science - 7 years
Python - 5 years
R Language - 3 years
Machine Learning - 6 years
Data Analysis - 6 years
TensorFlow - 3 years
Pytorch - 3 years
SciKit-Learn - 3 years
AWS - 3 years
Azure - 3 years
GCP - 3 years
Artificial Intelligence - 2 years