Data Scientist

About the Role

We are seeking a seasoned Data Scientist to develop, assess, and deploy predictive and intelligent models leveraging machine learning, deep learning, natural language processing, and computer vision. This position emphasizes crafting production-quality models, upholding statistical excellence, and transforming analytical insights into impactful business decisions.

Key Responsibilities

• Lead the end-to-end process of model creation, including experimentation, assessment, and readiness for deployment.
• Build diverse models such as forecasting, optimization, recommendation systems, decision science, and machine learning models.
• Conduct feature engineering, exploratory data analysis, and statistical evaluation to uncover significant data patterns.
• Establish thorough frameworks for evaluating, validating, and testing models rigorously.
• Ensure models meet standards in performance, fairness, and bias mitigation before deployment.
• Oversee experiment documentation, dataset management, and reproducibility with tools like MLflow or Weights & Biases.
• Communicate modeling outcomes, compromises, and uncertainties effectively to stakeholders.
• Collaborate with ML Engineers and System Architects for seamless model integration into production.
• Take charge of complex decision-intelligence projects as needed.
• Provide mentorship and guidance to junior data scientists and machine learning engineers.

Qualifications and Skills

• Minimum of five years applying data science with a proven record of deploying production-ready models.
• Deep expertise in classical/statistical machine learning, forecasting techniques, optimization strategies, and deep learning frameworks.
• Experience in NLP, computer vision, graph-related features, or knowledge graphs is advantageous.
• Proficiency in Python and SQL along with familiarity with scikit-learn, PyTorch or TensorFlow, MLflow, and Weights & Biases.
• Strong foundation in hypothesis testing, confidence interval estimation, and causal inference methodologies.
• Awareness of fairness and bias evaluation plus competence with explainability tools such as SHAP and LIME.
• Effective communication skills capable of simplifying technical model aspects and trade-offs for business audiences.
• Capability to justify modeling approaches and engage actively with engineering and architecture teams.