Lead Data Scientist
4 days ago
Position: Lead Data Scientist
Job Type: Full Time
Location: Remote (USA/Canada)
Job Description:
- Bayesian Modeling & Marketing Analytics:
- Proficiency in Bayesian inference and modeling, including prior knowledge incorporation and validation techniques.
- Ability to handle seasonality, long-term trends, and multi-touchpoint attribution in Marketing Mix Models (MMMs).
- Addressing collinearity in marketing channels.
- Big Data & ETL:
- Hands-on experience with tools like Databricks, Spark, and BigQuery.
- Expertise in designing and optimizing data pipelines for marketing data.
- Knowledge of real-time streaming data and partitioning strategies.
- General Machine Learning Fundamentals:
- Understanding the bias-variance tradeoff, class imbalance, feature selection, and cross-validation.
- Outlier detection and management techniques.
- Python (sklearn) and Classical ML Techniques:
- Experience with sklearn, including estimators, transformers, pipelines, and hyperparameter tuning.
- Knowledge of ensemble methods and handling categorical variables.
- LLMs & Generative AI:
- Experience with large language models (LLMs), including prompt engineering and retrieval-augmented generation (RAG).
- Use of LLMs for coding, research, and productivity enhancement.
- Neural Networks & PyTorch:
- Designing architectures for time-series forecasting.
- Implementation of custom loss functions, attention mechanisms, and optimization algorithms.
- Handling overfitting, learning rate scheduling, and batch normalization.
- Optimization Techniques:
- Knowledge of optimization methods (e.g., gradient descent, convex vs. non-convex optimization).
- Handling constraints, regularization, and competing objectives.
- Model Interpretability & Explainability:
- Use of interpretability tools (e.g., SHAP, LIME) and counterfactual analysis.
- Communicating feature importance and debugging complex models.
- Solution Architecture:
- Designing end-to-end marketing systems covering data ingestion, processing, modeling, and visualization.
- Scalability, versioning, compliance, internationalization, and A/B testing within marketing frameworks.
- Practical Problem-Solving:
- Debugging model issues, missing data handling, and performance optimization.
- Design and implementation of recommendation systems, anomaly detection, and real-time optimization models.
Preferred Experience
- Hands-on work with marketing teams, developing marketing mix models, or related analytical roles.
- Familiarity with tools and frameworks used in marketing data processing and modeling.
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