Data Scientist

Role Overview

We seek a seasoned Data Scientist to design, assess, and deploy predictive and intelligent models spanning machine learning, deep learning, natural language processing, and computer vision. The role involves creating production-grade models, ensuring statistical validity, and converting analytical results into actionable business insights.

Key Responsibilities

• Lead the entire lifecycle of model creation including development, experimentation, evaluation, and preparing models for production deployment.
• Develop models focused on forecasting, optimization, recommendation systems, decision science, and various machine learning applications.
• Conduct feature engineering, exploratory data analysis, and statistical examination to reveal significant patterns and trends.
• Establish comprehensive frameworks for rigorous model evaluation, validation, and testing processes.
• Ensure model fairness, performance, and bias considerations are addressed and validated before deployment.
• Oversee management of experiment tracking, datasets, and model reproducibility utilizing platforms like MLflow and Weights & Biases.
• Communicate findings related to model outputs, trade-offs, and uncertainties effectively to business stakeholders.
• Work collaboratively with machine learning engineers and architects to facilitate model production integration.
• Take lead roles in complex decision intelligence projects as necessary.
• Provide mentorship and guidance to junior data scientists and machine learning engineers.

Qualifications and Skills

• Minimum of five years of hands-on experience in applied Data Science with a proven track record of deploying production models.
• Expert knowledge in classical/statistical machine learning, forecasting methods, optimization techniques, and deep learning frameworks.
• Additional expertise in NLP, computer vision, graph-based feature creation, or knowledge graphs is advantageous.
• Proficiency in Python programming and SQL, with practical experience using scikit-learn, PyTorch or TensorFlow, plus MLflow or Weights & Biases tools.
• Solid understanding of statistical methodologies including hypothesis testing, confidence intervals, and causal inference techniques.
• Familiarity with tools for analyzing fairness, bias, and explainability such as SHAP and LIME.
• Excellent communication abilities to articulate complex technical concepts and model limitations to non-technical stakeholders.
• Capability to justify modeling decisions and maintain close cooperation with engineering and architectural teams.