Clinical Project Associate

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Job Overview

The Clinical Project Associate is responsible for accurately annotating, labelling and categorizing dermatological images, clinical findings, and related metadata to support the training and validation of Artificial Intelligence (AI)/Machine Learning (ML) models in Dermatology. This role requires strong attention to detail, understanding of dermatological features, and strict adherence to relevant medical annotation guidelines to ensure production of clinically reliable datasets. Adequate training and mentorship would be provided, considering that, annotation/labelling is still an evolving field in India.

Key Responsibilities

Dermatology-Specific Annotation Tasks

Annotate dermatological images with high precision (e.g., bounding boxes, segmentation masks, region-of-interest labels).

Classify skin conditions based on established taxonomies (e.g., acne grading, pigmentation types, lesions, erythema intensity, psoriasis scaling, fungal infections).

Tag clinical attributes such as lesion type, location, colour, size, pattern, and severity.

Annotate multi-modal dermatology data (clinical images, dermoscopic images, patient notes where applicable).

Ensure compliance with medical privacy standards while handling sensitive clinical images.

Quality & Data Consistency

Validate annotations for accuracy, completeness, and consistency against clinical guidelines.

Identify ambiguous or poor-quality images and flag them for review.

Participate in cross-checking and inter-annotator agreement exercises.

Maintain high-quality datasets used for AI training, validation, and benchmarking.

Collaboration & Documentation

Work closely with dermatologists, medical reviewers, and AI/ML engineers to refine annotation rules.

Provide feedback to improve taxonomy, clinical definitions, and annotating/labelling frameworks.

Update annotation protocols based on expert input and new project requirements.

Document edge cases, annotation challenges, and observed data patterns.

Qualifications

Bachelors degree in Life Sciences (e.g., MBBS, BAMS, BHMS), Biotechnology, or related fields.

Computer savvy with good understanding of basic dermatology concepts (common skin conditions, lesion morphology, skin types, severity scales).

Strong visual assessment skills and attention to detail in clinical images.

Ability to learn and apply dermatology annotation guidelines accurately.

Good communication skills for working with dermatologists and data teams.

Desired (Preferable) Qualifications

Basic understanding of AI/ML data processes (training sets, ground truth, segmentation).

Familiarity with dermatology terminologies like macules, papules, plaques, comedones, pigmentation patterns, etc.

Experience in tele-dermatology, clinical research, or skin imaging.

Ability to interpret dermoscopy images is a plus.

Additional Requirements

High accuracy and consistency in medical annotation/labelling

Fast learning of clinical concepts

Process and protocol adherence

Analytical mindset and pattern recognition

Ability to manage high-volume datasets

Collaboration with clinical experts