Senior Data Scientist

Trendlytics is a healthcare innovation company based in the UK and US, specializing in AI-driven predictive and prescriptive analytics for resource optimization. Our suite of products helps healthcare organizations streamline operations, improve patient flow, and enhance decision-making.

We are seeking a Senior Data Scientist to join our fast-paced, collaborative team. In this role, you'll apply advanced analytics and machine learning to solve complex healthcare challenges, driving meaningful impact on operational performance and patient outcomes.

Job Type: Remote

Responsibilities:

• Develop time-series forecasting models for patient risk prediction, hospital resource planning, and disease progression modeling.
• Apply machine learning and deep learning techniques to healthcare datasets for predictive analytics.
• Apply NLP techniques (transformers, clinical entity recognition, etc) to extract insights from unstructured data, including physician notes, radiology reports, and patient records.
• Work with real-time streaming data from ICUs, wearable devices, and hospital systems to build early warning systems for patient deterioration and anomaly detection.
• Deploy and run production-level models on Azure, AWS, or other cloud platforms, ensuring scalability, reliability, and compliance with healthcare data regulations.
• Implement MLOps practices for versioning, monitoring, and retraining ML models in production.
• Collaborate with clinicians, data engineers, and product teams to ensure models are interpretable, clinically relevant, and ethically deployed.
• Optimize and scale existing machine learning models for deployment in cloud-based environments.
• Effectively communicate complex data science concepts and insights to non-technical stakeholders, providing clear recommendations and action plans.

Requirements:

• Education: Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field (Master’s preferred).
• Experience: 3+ years in data science, with a strong focus on ML applications.
• Hands-on experience with time-series forecasting methods
• Proficiency in Python (pandas, NumPy, scikit-learn, statsmodels) and SQL.
• Familiarity with big data frameworks (Spark, Dask) and cloud platforms (AWS, GCP, or Azure).
• Experience deploying ML models in real-time or batch processing environments.
• Strong ability to gather, analyze, and translate business needs into actionable machine learning solutions.

Preferred Qualifications:

• Experience working with and modeling healthcare data, including structured (EHR, claims, lab results) and unstructured (clinical notes, imaging reports) datasets.
• Experience working with large language models (LLMs) for clinical text generation, summarization, and information extraction.
• Familiarity with fine-tuning LLMs on domain-specific data.
• Experience deploying LLMs in a production setting.

Join us and be part of a dynamic, growing team dedicated to transforming healthcare with innovative and impactful solutions!