Company Overview

CommerceIQ’s AI-powered digital commerce platform is revolutionizing the way brands sell online. Our unified ecommerce management solutions empower brands to make smarter, faster decisions through insights that optimize the digital shelf, increase retail media ROI and fuel incremental sales across the world’s largest marketplaces. With a global network of more than 900 retailers, our end-to-end platform helps 2,200+ of the world’s leading brands streamline marketing, supply chain, and sales operations to profitably grow market share in more than 50 countries. 10 out of the top 12 CPG brands work with us, including Coca-Cola, Nestle, Colgate-Palmolive, and Mondelez. We’ve raised over $200M from some of the top investors including SoftBank, Insight Partners, and Madrona. Learn more at commerceiq.ai.

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.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any other category prohibited by applicable law.

Data Scientist II

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