Requirements
Requires 5+ years of applied Data Science experience with production model-shipping experience. Strong expertise in classical/statistical ML, forecasting, optimization, and deep learning. Must have strong Python, SQL, and experience with scikit-learn, PyTorch/TensorFlow, MLflow, or Weights & Biases. Knowledge of hypothesis testing, confidence intervals, and causal inference is essential. Familiarity with fairness/bias evaluation and explainability tools is a plus. Strong communication skills are required to explain technical concepts to business stakeholders.
Description
We are looking for an experienced Data Scientist to build, evaluate, and productionize predictive and intelligent models across ML, deep learning, NLP, and computer vision. The role focuses on developing production-ready models, maintaining statistical rigor, and translating insights into business outcomes.Responsibilities:Own model development, experimentation, evaluation, and production readiness.Develop forecasting, optimization, recommendation, decision-science, and ML models.Perform feature engineering, EDA, and statistical analysis to identify meaningful patterns.Design rigorous model evaluation, validation, and testing frameworks.Validate model performance, fairness, and bias before go-live.Manage experiment tracking, datasets, and model reproducibility using tools such as MLflow/W&B.Present model results, trade-offs, and uncertainty to business stakeholders.Collaborate with ML Engineers and Architects to productionize models.Lead complex decision-intelligence engagements when required.Mentor junior Data Scientists and ML Engineers.Qualifications:5+ years of applied Data Science experience with production model-shipping experience.Strong expertise in classical/statistical ML, forecasting, optimization, and deep learning.Experience with NLP, Computer Vision, graph-based features, or knowledge graphs is a plus.Strong Python, SQL, and experience with scikit-learn, PyTorch/TensorFlow, MLflow, or Weights & Biases.Strong knowledge of hypothesis testing, confidence intervals, and causal inference.Familiarity with fairness/bias evaluation and explainability tools such as SHAP and LIME.Strong communication skills with the ability to explain technical concepts and model trade-offs to business stakeholders.Ability to defend modeling decisions and collaborate closely with engineering and architecture teams.