Data Scientist II

6 days ago

Bengaluru, Karnataka, India CommerceIQ Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Technical Expertise:

  • Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc.).
  • Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.
  • Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem and good to have distributed training frameworks (e.g., DeepSpeed, PyTorch Lightning, Ray).
  • Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
  • Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
  • Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.

Applied Problem-Solving:

  • Mandatory skill - Demonstrated success in adapting foundation models to domain-specific applications through fine-tuning or transfer learning.
  • Mandatory skill - Strong ability to design, evaluate, and improve models using robust validation strategies, bias/fairness checks, and performance optimization techniques.
  • Experience in working on applied AI problems across NLP, computer vision, or multimodal systems or any other domain.

Leadership & Collaboration:

  • (Preferred) Proven ability to lead and mentor a junior applied scientists and ML engineers, providing technical guidance and fostering innovation.
  • Strong cross-functional collaboration skills to work with product, engineering, and business stakeholders to deliver impactful AI solutions.
  • Ability to translate cutting-edge research into practical, scalable solutions that meet real-world business needs.

Education & Experience:

  • 3 years of hands-on experience in applied machine learning and data science with Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field or appropriate experience.
  • Excellent communication and presentation skills to articulate complex ML concepts to both technical and non-technical audiences.
  • Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques.

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