Data Scientist - Medical Imaging

Job Requirements

At Quest Global, its not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better placeto make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.

Key Responsibilities

• Clinical Imaging AI

  : Design and implement ML/DL pipelines for MR imaging tasks (segmentation, classification, anomaly detection), including quantitative image analysis.

• Data &

  DICOM

  : Build preprocessing pipelines for

  MR and 3D

  DICOM

  data; implement reliable data ingestion, parsing, deidentification, and curation across structured, unstructured, audio, static, and streaming datasets.

• Model Excellence

  : Ensure model

  explainability

  , statistical rigor, bias checks, and robust validation (crosssite, crossscanner) in line with clinical and regulatory/quality expectations.

• Foundation & Vision Models

  : Finetune and adapt modern vision and multimodal models (e.g.,

  Vision Transformers, CLIP, MedSAM, BioGPT

  or similar) for diagnostic and clinical applications.

• GenAI & LLMs

  : Lead initiatives in

  Generative AI

  and

  LLMs

  (pretraining, finetuning, domain adaptation, agentbased workflows) for imagingcentric and multimodal solutions; integrate

  NLP/NLU

  for clinical text and reporting.

• Production & Cloud

  : Operationalize models on

  AWS/Azure

  ; contribute to MLOps (versioning, CI/CD, monitoring, drift detection, rollback) with secure, compliant deployments.

• Experimentation & Research

  : Stay current with ML/GenAI research; evaluate SOTA methods and translate promising approaches into productionready features.

• Stakeholder Engagement

  : Translate business challenges into ML problem statements; articulate design decisions, tradeoffs, and outcomes to clinical, product, and business stakeholders.

• Quality & Risk

  : Partner with QA/RA teams on documentation, validation protocols, and audit readiness for clinical use.

• Collaboration

  : Work closely with radiology experts and imaging engineers to align solutions with

  clinical workflows

  and

  radiology practices

  , particularly for MR.

We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.

We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.

Work Experience

Education & Experience

• PhD

  in CS/EE/Biomedical/Medical Imaging/Data Science or related field

  and 5+ years

  of postdegree experience in AI/ML for medical imaging;

  or

• M.Tech

  in CS/EE/Biomedical/Medical Imaging/Data Science or related field

  and 8+ years

  of relevant experience.

Required Skillset

• Deep domain knowledge

  of medical imaging modalities,

  MR

  physics/workflows, and radiology practices.

• Advanced

  Python

  programming; strong in

  TensorFlow

  and/or

  PyTorch

  ; familiarity with medical image processing tools/libraries.
• Proven track record deploying

  clinicalgrade AI/ML

  solutions with

  explainability

  ,

  validation

  , and

  regulatory/quality

  considerations.
• Handson

  DICOM

  parsing, annotation tooling, clinical data curation/management.
• Solid

  statistical modeling

  ,

  feature engineering

  , and applied ML for imaging data.
• Practical

  data augmentation

  and preprocessing strategies for MR and other modalities; rigorous

  validation

  methodologies.
• Experience developing and deploying ML on

  AWS or Azure

  .
• Advanced proficiency in

  NLP/NLU

  .
• Strong command of

  Generative AI

  and

  LLMs

  (pretraining, finetuning, domain adaptation, agentbased workflow design).
• Ability to convert

  business challenges

  into ML problem statements and ship endtoend solutions.
• Clear communication with both technical and business stakeholders.

Good to have

• Experience with

  foundation/vision models

  (e.g., Vision Transformers,

  CLIP

  ,

  MedSAM

  ,

  BioGPT

  , or similar multimodal models) for diagnostic imaging.
• Exposure to

  IEC 62304

  ,

  ISO 13485

  ,

  HIPAA/GDPR

  , and healthcare data security practices.
• Familiarity with clinical data standards (e.g.,

  HL7/FHIR

  ) and PACS/RIS integration.
• MLOps tooling (MLFlow, Weights & Biases, Kubernetes, Docker) and production monitoring for imaging AI.
• Publications or patents in medical imaging AI; contributions to opensource imaging or ML frameworks.