Designation : Consultant
Location : San Jose, California , United States
Experience : 6 to 15 years
Job Role : Build, optimize, and operate production machine-learning models that power B2B GTM prioritization. You'll work across the full lifecycle - feature engineering on multi-billion-row data, model training and tuning, deployment, monitoring, and iterating on existing models in production.
This is a hands-on role on a small team. You'll own model quality end-to-end, partner with data engineers and analysts, and present findings to business stakeholders.
Required qualifications Machine Learning: Strong fundamentals in supervised learning, especiallytree-based models(XGBoost, LightGBM, Random Forest)
Experience training, tuning, and evaluating classification models on imbalanced datasets
Comfort with model explainability techniques (SHAP, feature importance, partial dependence)
Hyperparameter optimization (Optuna, Hyperopt, or grid/random search)
Understanding of model persistence, versioning, and deployment patterns
Programming: Python- pandas, NumPy, scikit-learn, modern packaging practices
SQL- advanced (window functions, CTEs, complex joins, performance tuning on large datasets)
Comfort reading and refactoring legacy code
Data platforms - Experience with Databricks or a comparable cloud-data platform (Snowflake, BigQuery, EMR)
- Working knowledge ofApache Spark(PySpark or Spark SQL)
- Familiarity with Delta Lake, Parquet, or similar table formats
- Notebook-driven development workflows
Engineering practices - Version control (Git), code review, testing
- Writing maintainable code that other engineers will read and extend
- Documentation habits - clear comments, design notes, handoff docs
Preferred qualifications - Experience working with B2B / sales data- CRM, marketing automation, opportunity pipelines, account-level analytics
- Exposure toAdobe Experience Platform, Salesforce, Marketo, or similar enterprise data sources
- Background inpropensity modeling, lead scoring, or engagement scoring
- Experience with MLOps tooling (MLflow, model registries, scheduled retraining pipelines)
- Familiarity withUnity Catalogor other governed data platforms
- Comfort with experimentation frameworks and A/B testing
Responsibilities : What you'll do
- Maintain and improve a portfolio of production scoring models
- Optimize data pipelines for performance and cost on large-scale datasets
- Collaborate with data engineers, analysts, and business stakeholders to translate requirements into model designs
- Investigate data quality issues and assess their impact on model performance
- Contribute to shared infrastructure that supports the broader ML team
- Communicate technical findings clearly to non-technical audiences
Required skills : Machine Learning, B2B , Python,SQL
