Lead Data Scientist

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Lead Data Scientist Bengaluru, Karnataka, India The Company CommerceIQ is building the AI platform that runs commerce for the world's largest brands. We are not selling AI demos. We are shipping AI agents for content, media, and sales into the workflows of the Fortune 100 every week. 2,200+ Customers 10 of Top 12 CPG Companies 900+ Retailers Connected $200M+ Raised Customers include Coca-Cola, Nestlé, Colgate-Palmolive, Mondelez, Samsung, and Kellogg's. Backed by SoftBank, Insight Partners, and Madrona. Headquartered in Mountain View with teams across the US, India, Canada, and the UK. Pre-IPO. 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,

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