Data Science Lead (MLOps & Advanced Analytics)

Architectural Leadership: Lead the design and delivery of end-to-end ML systems, prioritizing MLOps principles to ensure model reproducibility, auditability, and scalability.

Full-Lifecycle Development: Oversee the journey from hypothesis and Exploratory Data Analysis (EDA) to feature engineering, model selection, and production deployment.

Cross-Functional Synergy: Act as the technical bridge between Data Engineers (for ETL/Feature Store optimization) and Business Stakeholders (to translate KPIs into objective functions).

Infrastructure Automation: Architect automated pipelines for data validation, model profiling, and hyperparameter tuning using Azure Machine Learning Services.

Governance & Monitoring: Establish rigorous monitoring for Data Drift and Concept Drift, ensuring model performance remains optimal post-deployment.

Experience: 5–8 years of total experience, with 4+ years specifically in a hands- on Data Science role and 2+ years leading technical teams or complex projects.

Education: BE/BS or MS/PhD in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.

1. Advanced Modeling & Mathematics- Deep Learning & Classical ML: Proficiency in supervised/unsupervised learning, including Gradient Boosted Trees (XGBoost/LightGBM), Random Forests, and Neural Networks.

Statistical Rigor: Mastery of hypothesis testing, Bayesian inference, and error analysis. Ability to design complex experiments and A/B tests.

Time Series & Forecasting: Experience with advanced forecasting (Prophet, ARIMA, or LSTM networks) is highly desirable for Supply Chain/Revenue applications.

Optimization: Knowledge of loss function customization and optimization algorithms (Gradient Descent, Genetic Algorithms).

2. Engineering & MLOps (The 'Lead Edge)- The Stack: Advanced proficiency in Python (Pandas, Scikit-learn, PySpark/TensorFlow) and SQL.

Big Data: Hands-on experience with PySpark and Databricks for distributed processing of petabyte-scale datasets.

Orchestration: Expert-level knowledge of MLFlow for experiment tracking and Kubeflow or Azure Pipelines for orchestration.

Deployment: Experience with containerization (Docker/Kubernetes) and deploying models batch inference jobs.

Strategic Translation: The ability to take an ambiguous business problem (e.g., 'We are losing margin in the Midwest') and translate it into a specific ML problem (e.g., 'A multi-classification churn model with a SHAP-based interpretability layer').

Technical Mentorship: A proven track record of conducting code reviews, promoting best practices in ''Clean Code', and upskilling junior data scientists.

Agile Advocacy: Deep familiarity with Agile/Scrum methodologies, specifically how to adapt 'Sprint'; cycles to the non-linear nature of Research & Development.

Influence & Stakeholder Management: The 'soft power' to explain complex model trade-offs (e.g., Precision vs. Recall) to non-technical executives to drive data-driven decision-making.