Lead Data Scientist at CommerceIQ

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, NLP, Parameter-Efficient Fine-Tuning, PyTorch, TensorFlow, Hugging Face, MLOps, Data Pipelines, Feature Engineering, GPU Optimization, Model Evaluation, Applied AI, Leadership, Collaboration, Communication

Industry

Software Development

Description

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 Proven ability to lead and mentor a team of 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. Other 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. Education & Experience Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field. 5+ years of hands-on experience in applied machine learning and data science. 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.

Responsibilities

Lead a team of applied scientists and ML engineers to develop AI solutions. Collaborate with cross-functional teams to translate research into practical applications.