AI Researcher / Senior Data Scientist

About Seegnal:

Seegnal is a pioneering health-tech company dedicated to transforming clinical decision-making and

enhancing patient safety. By leveraging advanced analytics and an innovative, user-centric platform, we

empower healthcare professionals to navigate complex clinical data with precision and ease. Join our

cross-functional team to help shape the future of digital health and drive meaningful, data-backed

impact in the healthcare industry.

About This Role:

This is a high-impact role within Seegnal’s core AI group, which specializes in developing cutting-edge

AI solutions for healthcare with a focus on enhancing prescription safety. As an AI Researcher, you will

play a key role in our AI research and development efforts, with deep hands-on involvement in critical

initiatives.

You will be responsible for the design and implementation of sophisticated AI projects. As a strong

technical contributor, you will help drive successful execution and find creative, robust solutions to

complex bottlenecks that span across data, AI, engineering, and product considerations.

Responsibilities:

• Algorithm Development: Design, develop, and implement accurate, robust AI/Deep Learning algorithms for medical data using a hands-on approach.

• Research & Experimentation: Design and conduct rigorous research and experimentation to continuously improve algorithm performance, scalability, and accuracy.

• Cross-Functional Collaboration: Partner closely with data scientists, software engineers, product managers, and clinical data experts to ensure AI initiatives have a profound, aligned impact on R&D and product decisions.

• Innovation: Stay at the forefront of the latest advancements in AI and machine learning, particularly within the medical domain, and help drive their adoption within the team.

Requirements

• Education: Master's or Ph.D. degree in Computer Science, Bioinformatics, Data Science, Statistics, or a related quantitative field.

• Experience: 5+ years of hands-on experience designing and implementing advanced Data Science, Machine Learning, and AI/DL algorithms.

• Healthcare Data Science & Analytics: Deep technical expertise in mining, cleaning, and analyzing noisy, high-dimensional healthcare datasets (e.g., Electronic Health Records, claims data, clinical trials). Proven ability to perform advanced feature engineering, statistical modeling, and extract actionable, data-driven clinical insights from complex medical data.

• Advanced ML & Deep Learning for Tabular Data: Extensive hands-on experience designing, training, and optimizing both classical machine learning models (e.g., XGBoost, LightGBM, CatBoost, Random Forests) and Deep Learning architectures specifically tailored for rich, tabular medical data. Deep proficiency in leveraging these tree-based ensembles alongside PyTorch-based custom neural networks to derive predictive insights and risk stratification from structured clinical datasets.

• End-to-End Cloud MLOps: Comprehensive knowledge of the entire machine learning lifecycle, from problem definition and data extraction to model training, serving, and continuous monitoring. Proven experience deploying, scaling, and maintaining models in Google Cloud Platform (GCP) (e.g., Vertex AI, BigQuery) or equivalent cloud environments (AWS/Azure).

• Technical Skills: Deep proficiency in Python and SQL, alongside hands-on experience with modern AI/ML frameworks PyTorch. Proven Track Record: Demonstrated experience contributing to AI projects from the research phase through to deployment and commercial success.

• Problem Solving: Exceptional analytical and problem-solving skills, with the ability to navigate difficult engineering and data bottlenecks.

• Soft Skills: Excellent communication and collaboration skills, with a proven ability to articulate complex technical concepts to non-technical stakeholders and work both independently and cross functionally.

• Advantage: Prior experience in the healthcare industry, specifically in prescription management, pharmacovigilance, or clinical data platforms, is a strong advantage. Additionally, a background or degree in Pharmacy is highly valued.