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Capgemini
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Data Scientist- Computer Vision

Data Scientist- Computer Vision

Capgemini

6 - 9 years

9 - 18 Lacs

bengaluru

Posted:3 weeks ago| Platform:

Apply

Skills Required

computer vision artifiacial microsoft azure python

Work Mode

Hybrid

Job Type

Full Time

Job Description

About the Role

Computer Vision

Key Responsibilities

Problem Framing & EDA

Translate business objectives into CV/AI problems; define success metrics.
Conduct
exploratory data analysis
on image/video datasets; assess data quality, bias, and labeling strategies.

Modeling & Algorithms

Build CV models for
image classification
,
object detection
,
segmentation
,
facial recognition
, and
OCR
using
PyTorch/Torch
,
TensorFlow/Keras
, and
OpenCV
.
Apply
deep learning
techniques: CNNs, transformers (ViT/DETR), self-supervised learning, metric learning, multi-modal (audiovisual) pipelines.

Training, Evaluation & MLOps

Implement robust
model training
,
hyperparameter tuning
,
cross-validation
, and
model evaluation
(precision/recall, F1, mAP, AUC, Dice/IoU).
Optimize performance (quantization/pruning/distillation) and latency for real-time
video content analysis
and edge scenarios.
Productionize models with
cloud application platforms
(AWS SageMaker, GCP Vertex AI, Azure ML), CI/CD, experiment tracking,
model monitoring
, and retraining workflows.

Data & Infrastructure

Build data pipelines for
digital imaging
and
medical imaging
(DICOM) ingestion, preprocessing, augmentation, labeling tools, and dataset versioning.
Leverage
cloud computing
services: object storage, serverless, containers (Docker/Kubernetes), scalable inference endpoints, and security-first design.

Stakeholder & Client Management

Collaborate with product managers, clinicians, media partners, and customers to gather requirements, communicate results, and influence roadmaps.
Present findings with clear
customer communication
and
stakeholder management
; contribute to documentation and research write-ups.

Security & Compliance

Uphold data privacy, ethics, and
security
best practices; support compliance for regulated domains (esp.
health technology
).
Role & responsibilities

Languages & Libraries:
Python, PyTorch/Torch, TensorFlow/Keras, OpenCV, scikit-learn, NumPy/Pandas.
Model Ops:
MLflow/W&B, Docker, Kubernetes, CI/CD.
Cloud:
AWS (SageMaker, S3, ECS/EKS, Lambda), GCP (Vertex AI, GCS, Cloud Run), Azure (Azure ML, Blob, AKS).
Data & Labeling:
DVC, CVAT/Label Studio, DICOM toolkits, ffmpeg.
Monitoring:
Prometheus/Grafana, custom drift dashboards.

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