Lead Data Scientist

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 problem-solving, 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