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This role is for a Classic AI/ML Data Scientist to build, validate, and deploy predictive and statistical machine learning models. You will focus on traditional modeling techniques that drive core business decisions and integrate with the AI ecosystem.
Virtusa
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This is a remote position.
We are seeking a Classic AI/ML Data Scientist to build, validate, and deploy predictive and statistical machine learning models. While our agentic team focuses on LLM orchestration, you will own traditional supervised/unsupervised modeling-such as forecasting, classification, clustering, anomaly detection, and propensity scoring-that drives core business decisions and feeds signals into our AI ecosystem.
Key Responsibilities:
Frame business problems as machine learning tasks and conduct exploratory data analysis (EDA) on large-scale structured datasets.
Develop, train, and validate predictive models (gradient boosting, time-series forecasting, regression, clustering, NLP).
Build reproducible feature engineering and model training pipelines in Python and SQL.
Collaborate with Data Engineers to deploy models into production with automated monitoring for data drift and model degradation.
Communicate model insights, confidence intervals, and business impact clearly to non-technical stakeholders.
4+ years of hands-on experience building and deploying classical machine learning and statistical models in production.
Strong proficiency in Python (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow, statsmodels, pandas) and SQL.
Solid foundation in statistics, experimental design (A/B testing), feature engineering, and model evaluation metrics.
Experience working in cloud data environments (Databricks, Snowflake, AWS, or Azure) and using MLOps tools (MLflow, Git).
Pragmatic mindset focused on choosing the simplest, most reliable model that solves the business problem.
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Virtusa
Virtusa
Virtusa
