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.
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Frame business problems as machine learning tasks and conduct exploratory data analysis (EDA) on large-scale structured datasets.
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Develop, train, and validate predictive models (gradient boosting, time-series forecasting, regression, clustering, NLP).
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Build reproducible feature engineering and model training pipelines in Python and SQL.
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Collaborate with Data Engineers to deploy models into production with automated monitoring for data drift and model degradation.
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Communicate model insights, confidence intervals, and business impact clearly to non-technical stakeholders.
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Work Schedule: During the onboarding phase, working hours may be adjusted to overlap with US hours until 7:00 PM at the latest.
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4+ years of hands-on experience building and deploying classical machine learning and statistical models in production.
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Strong proficiency in Python (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow, statsmodels, pandas) and SQL.
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Solid foundation in statistics, experimental design (A/B testing), feature engineering, and model evaluation metrics.
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Experience working in cloud data environments (Databricks, Snowflake, AWS, or Azure) and using MLOps tools (MLflow, Git).
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Pragmatic mindset focused on choosing the simplest, most reliable model that solves the business problem.
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