Staff Applied Scientist
Salary not listed • Hyderabad, Telangana, India • Full-time
Posted 5mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Hyderabad, Telangana, India
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
Cohere Health is seeking a Staff Applied Scientist to play a critical technical leadership role in powering and scaling machine learning capabilities across their products. This role involves applying state-of-the-art machine learning, large language models, and agentic architectures to complex clinical and operational workflows to automate decision-making, improve data quality, and reduce administrative burden. You will partner closely with clinical operations, product, and engineering teams to uncover insights, inform strategic decisions, and design and evaluate ML systems that directly impact member and provider experiences.
This position blends deep hands-on technical work with strategic influence, mentorship, and ownership across multiple work streams, while contributing to broader ML initiatives. The ideal candidate will have a Master's degree (PhD preferred) in a quantitative field and 7+ years of professional experience in applied machine learning or data science, with a strong understanding of Python, deep learning frameworks like PyTorch, and experimental design for production environments.
Responsibilities
- Design, develop, and evaluate advanced machine learning systems for retrieval, classification, prediction, and generative use cases.
- Apply advanced statistical and ML techniques to extract insights from large-scale structured and unstructured healthcare datasets.
- Lead model development across the ML lifecycle, including experimentation, training, evaluation, and partnering with AI engineering for deployment, monitoring, and iteration.
- Write and maintain reusable codebases for data preprocessing, model training, evaluation, and deployment.
- Collaborate cross-functionally with product managers, clinicians, data engineers, BI engineers, and design teams to translate needs into ML solutions.
- Drive experimentation by defining problem statements, forming hypotheses, and designing evaluation frameworks tied to business outcomes.
- Review, communicate, and present ML insights and results to technical and non-technical stakeholders.
- Serve as a technical mentor and advisor to junior engineers on ML best practices, experimentation, and system design.
- Contribute as an expert advisor across multiple initiatives, shaping ML strategy and performance tracking.
Requirements
- Master's degree (PhD preferred) in Computer Science, Data Science, Machine Learning, or a closely related quantitative field.
- 7+ years of professional experience in applied machine learning or data science, including ownership of production ML systems.
- Deep expertise in Python and modern deep learning frameworks (e.g., PyTorch).
- Hands-on experience building and evaluating deep learning models (e.g., transformers) for NLP tasks.
- Strong understanding of experimental design, model evaluation, and optimization for real-world production environments.
- Experience leveraging cloud platforms (AWS preferred) across the ML lifecycle (training, deployment, monitoring).
- Proven ability to collaborate with product, business, and clinical partners to drive data-informed decision-making.
- Excellent written and verbal communication skills, with experience presenting to both technical and non-technical audiences.
- Experience with generative AI, large language models, agentic systems, or Retrieval Augmented Generation (RAG) is a plus.
- Experience driving automation in healthcare or regulated environments is a plus.
- Familiarity with healthcare workflows such as claims, coding, utilization management, or network operations is a plus.
- Experience working with unstructured healthcare data (e.g., clinical notes, OCR, document understanding) is a plus.
- Hands-on experience with AWS tools such as SageMaker Studio is a plus.