Lead Data Scientist

4 days ago


Canada, CA NexGen Tech Solutions Full time

Position: Lead Data Scientist

Job Type: Full Time

Location: Remote (USA/Canada)


Job Description:

  1. 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.
  1. 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.
  1. General Machine Learning Fundamentals:
  • Understanding the bias-variance tradeoff, class imbalance, feature selection, and cross-validation.
  • Outlier detection and management techniques.
  1. Python (sklearn) and Classical ML Techniques:
  • Experience with sklearn, including estimators, transformers, pipelines, and hyperparameter tuning.
  • Knowledge of ensemble methods and handling categorical variables.
  1. 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.
  1. 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.
  1. Optimization Techniques:
  • Knowledge of optimization methods (e.g., gradient descent, convex vs. non-convex optimization).
  • Handling constraints, regularization, and competing objectives.
  1. Model Interpretability & Explainability:
  • Use of interpretability tools (e.g., SHAP, LIME) and counterfactual analysis.
  • Communicating feature importance and debugging complex models.
  1. 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.
  1. 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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