01Responsibilities
Analyze large and complex datasets to extract actionable insights, applying solid statistical foundations, advanced analytics, and domain aware interpretationDesign, develop, and deploy machine learning and deep learning models across diverse business and healthcare use cases, ensuring rigor in feature engineering, model training, evaluation, and explainabilityBuild production grade ML solutions using robust, maintainable Python and SQL, with proper versioning, monitoring, performance governance, and scalable deployment patternsApply classical ML techniques (logistic regression, decision trees, clustering, PCA, Bayesian models, time series models) and advanced methods such as survival analysis and complex statistical modelingDevelop deep learning solutions using TensorFlow, PyTorch, Keras, or XGBoost for NLP, speech, image processing, and multimodal workloads; implement CNNs, RNNs, LSTMs/GRUs, and optimization/regularization techniquesWork with Hugging Face Transformers, embeddings, sequence models, and NLP/NLU pipelines to support generative and discriminative tasksBuild and optimize recommender systems using collaborative filtering, sequence aware models (FPMC, FISM, Fossil), and deep recommendation architecturesExplore and integrate graph machine learning techniques and knowledge graphs to model complex entity relationships, reasoning, and advanced analyticsLeverage AutoML tools (H2O.ai, Vertex/Google Cloud AutoML, DataRobot) to accelerate experimentation while maintaining scientific rigor and model qualityApply healthcare data literacy to ensure compliant, domain aware model development, including familiarity with ICD, CPT, NDC, SNOMED, LOINC, FHIR, and HL7 datasetsGenerate synthetic datasets using tools like Gretel.ai or Synthea to support experimentation where real data is limited or sensitiveCollaborate with data engineering teams to ensure high quality feature pipelines, correct transformations, and production ready integrationsLead model governance practices, including documentation, model cards, validation reviews, responsible AI considerations, and continuous performance monitoring .
