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We are building the operating system for brands to win in E-commerce.
The Role
Design, fine-tune, and deploy large-scale ML models (LLMs, transformers, diffusion); apply PEFT techniques; build data pipelines and MLOps practices; optimize GPU/TPU training; validate models for performance and fairness; collaborate cross-functionally and mentor junior team members.
Summary Generated by Built In
The Company
Technical ExpertiseCommerceIQ 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.
- Strong background in machine learning, statistical modeling, predictive analytics, and feature engineering, with hands-on experience developing and deploying classical ML models.
- Strong proficiency in classical machine learning algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), Naive Bayes, K-Means, and DBSCAN.
- Experience with feature engineering, feature selection, dimensionality reduction, hyperparameter optimization, cross-validation, model calibration, and model evaluation.
- Proficiency in Python, Pandas, NumPy, Scikit-learn, with experience using libraries such as XGBoost, LightGBM, CatBoost, and Statsmodels.
- Experience working with large datasets and data pipelines, including data preprocessing, data quality checks, transformation, aggregation, and feature generation using tools such as SQL, Spark/PySpark, and cloud data platforms.
- Strong understanding of statistical methods and model diagnostics, including hypothesis testing, confidence intervals, correlation analysis, distribution analysis, and statistical significance.
- Experience with model deployment, monitoring, retraining, and productionization of classical machine learning models.
- Mandatory skill — Demonstrated experience building and deploying classical machine learning models for real-world business problems such as customer churn, credit risk, fraud detection, demand forecasting, customer segmentation, recommendation systems, pricing, propensity modeling, or sales prediction.
- Mandatory skill — Strong ability to design, evaluate, and improve ML models using robust validation strategies, cross-validation, hyperparameter tuning, feature engineering, and model comparison.
- Experience selecting appropriate algorithms based on business objectives, data characteristics, interpretability requirements, and model performance.
- Strong understanding of model evaluation metrics such as ROC-AUC, PR-AUC, Precision, Recall, F1, Log Loss, RMSE, MAE, MAPE/WMAPE, Gini, KS, R², and other domain-specific metrics.
- Experience with model interpretability and explainability, using techniques such as SHAP, Partial Dependence Plots (PDP), feature importance, permutation importance, and coefficient analysis.
- Experience identifying and addressing data quality issues, class imbalance, overfitting, multicollinearity, feature leakage, model bias, distribution shift, and model drift.
- Experience applying statistical and machine learning techniques to NLP, time-series forecasting, classification, regression, clustering, recommendation, or optimization problems.
- Preferred: Proven ability to mentor junior data scientists or analysts, provide technical guidance, and establish best practices for machine learning development.
- Strong cross-functional collaboration skills with product, engineering, business, and analytics stakeholders to translate business problems into measurable ML solutions.
- Ability to communicate model assumptions, methodology, results, limitations, and business impact to both technical and non-technical stakeholders.
- Ability to translate analytical findings into practical, scalable, and measurable business solutions.
- 1+ years of hands-on experience in classical machine learning, data science, predictive modeling, or statistical modeling.
- Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Engineering, or a related field, or equivalent practical experience.
- Strong analytical and problem-solving skills with the ability to work with structured and unstructured datasets.
- Excellent communication and presentation skills, with the ability to explain complex analytical and statistical concepts clearly.
- Strong understanding of machine learning fundamentals, statistics, probability, and optimization.
- Continuous learner with awareness of classical machine learning techniques, statistical modeling methodologies, model interpretability, and emerging best practices in applied 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.
Skills Required
- 3+ years hands-on experience in applied machine learning and data science
- Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or related field (or equivalent experience)
- Strong background in machine learning, deep learning, and NLP with experience training and fine-tuning large-scale models (LLMs, transformers, diffusion models)
- Hands-on expertise with Parameter-Efficient Fine-Tuning approaches (LoRA, prefix tuning, adapters) and quantization-aware training
- Proficiency in PyTorch, TensorFlow, and the Hugging Face ecosystem
- Experience with distributed training frameworks (DeepSpeed, PyTorch Lightning, Ray)
- Basic understanding of MLOps best practices (experiment tracking, model versioning, CI/CD for ML, production deployment)
- Experience working with large datasets, feature engineering, and data pipelines using tools such as Spark, Databricks, or cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML)
- Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments
- Demonstrated success adapting foundation models to domain-specific applications through fine-tuning or transfer learning
- Strong ability to design, evaluate, and improve models using robust validation strategies, bias/fairness checks, and performance optimization techniques
- Experience on applied AI problems across NLP, computer vision, or multimodal systems
- Excellent communication and presentation skills
- Proven ability to lead and mentor junior applied scientists and ML engineers
- Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques
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The Company
What We Do
CommerceIQ is the leader in E-commerce Channel Optimization (ECO), the practice of using machine learning, analytics and automations to optimize the e-commerce channel across supply chain, marketing and sales operations to win at the moment of purchase and drive profitable market share growth.
Why Work With Us
CommerceIQ is building innovative products in a hot market, in a pre-IPO company with a bold vision, and with passionate colleagues who are collectively building a company for the long run. We have developed a culture to support our growth and deliver a unique employee experience with market competitive pay and exceptional benefits.
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