Principal Data Scientist - R01554696

Primary Skills

• Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

Job requirements

• Qualifications Required: Master’s plus degree in Computer Science, Statistics, Applied Mathematics, or a related field. 7+ years of experience in data science and machine learning, with a proven track record of delivering models to production. Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow. Strong understanding of statistical modeling, machine learning algorithms, and experiment design. Solid experience with SQL and data manipulation tools (e.g., Pandas, Spark, or Dask). Experience deploying models using APIs (Flask, FastAPI), Docker, and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Hands-on experience with cloud platforms (AWS, GCP, or Azure) and model serving tools. Excellent problem-solving and communication skills; able to explain complex concepts clearly and effectively. Preferred: Familiar with McDonald’s business Experience with time series forecasting, causal inference, recommendation systems, or NLP. Familiarity with data versioning and reproducibility tools (e.g., DVC, Weights & Biases). Exposure to feature stores, streaming data (e.g., Kafka), or real-time ML systems. Background in MLOps and experience building generalizable ML frameworks or platforms. Here is some additional context that we have put together regarding what we are looking for: Core Technical Skills ML Engineer Preferred: Ideally, the candidate should be an ML Engineer, though seasoned Data Scientists with relevant experience are suitable. Python & SQL: Strong coding and data manipulation skills. Time-Series Forecasting: Experience with LGBM (LightGBM) and Darts library. MLOps Expertise Preferred: Hands-on experience with Astronomer, Airflow, and DAG creation. Capable of building wrappers and scalable pipelines. This skill is highly valuable, but not a deal breaker. Cloud Platforms: Proficient in AWS, with exposure to GCP preferred. Debugging & Troubleshooting: Skilled in investigating and resolving issues in Python experiments and executions. GitHub Proficiency: Comfortable working in repositories with many contributors, managing branches, pull requests, and code reviews. Collaboration & Work Style Self-Starter: Able to work independently and proactively contribute ideas. Team-Oriented: Willing to support Roman and Calvin while offering directional guidance on model enhancements. Fast Learner: Quick to adapt to new tools, workflows, and business contexts to rapidly onboard into the project. Domain Expertise Sales Forecasting: Proven experience in building and refining forecasting models. Understanding of business KPIs and translating insights into action.