Data Scientist II
Read straight from CommerceIQ's careers page, verified 1d ago.Seekless can tailor your résumé to it.
Seekless can write your résumé for this posting first.
Before you apply
LocationBengaluru, Karnataka, India
Typefull-time
SeniorityMid
Experience3+ yrs
DepartmentData Science
Company size51–200 people
PostedNov 2, 2026 · 6d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
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
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
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
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
3+ 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
Ability to mentor junior data scientists or analysts, provide technical guidance, and establish best practices for machine learning development
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
Python
Pandas
NumPy
Scikit-learn
XGBoost
LightGBM
CatBoost
Statsmodels
SQL
Spark
PySpark
SHAP
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, 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.
Applied Problem-Solving
•
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.
Leadership & Collaboration
Nice to have
•
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.
Education & Experience
•
3+ 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.
Legal
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.