Experience Range: With at least 2 to 4 years of hands-on experience in advanced data science, machine learning, and AI engineering roles Key Responsibilities:
Design and develop advanced machine learning models using classic algorithms and deep learning techniques to address complex business challenges and deliver measurable improvementsConduct comprehensive exploratory data analysis (EDA) and statistical analysis, including hypothesis testing, regression, and classification, to extract actionable insights from large datasetsImplement, optimize, and deploy AI/ML models on Google Cloud Platform (GCP), ensuring scalability, reliability, and efficient integration into production environmentsCollaborate with cross-functional teams to define data requirements, validate model outputs, and integrate AI solutions seamlessly into existing workflowsUtilize KubeFlow and BentoML for efficient model orchestration, deployment, and monitoring, ensuring robust operational performancePerform rigorous forecasting using methods such as exponential smoothing, ARIMA, and ARIMAX to support data-driven business planningApply probabilistic graph models and advanced statistical methods to enhance predictive accuracy and interpretability of AI solutionsMaintain high standards for data quality and model performance using frameworks like Great Expectations and Evidently AI, tracking key metrics and outcomesRequired Skills:
Proficiency in Python and SQL for data manipulation, analysis, and model developmentHands-on experience with classic machine learning algorithms and deep learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNetExpertise in statistical analysis including hypothesis testing, T-Test, Z-Test, and regression (linear, logistic)Experience with classification techniques such as decision trees and support vector machines (SVM)Knowledge of forecasting methods including exponential smoothing, ARIMA, and ARIMAXAbility to implement and interpret probabilistic graph modelsFamiliarity with tools for model deployment and orchestration such as KubeFlow and BentoMLCompetence in computing and analyzing distance metrics (Hamming, Euclidean, Manhattan)Experience with data quality frameworks such as Great Expectations and Evidently AIPreferred Skills:
Experience with GenAI and Agentic AI technologiesHands-on expertise with PySpark, SAS, or SPSS for large-scale statistical computingProficiency in R and R Studio for statistical modeling and data visualizationDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related disciplineCertification in machine learning or data science from recognized platforms such as TensorFlow Developer Certificate or Google Professional Machine Learning EngineerCertification in cloud technologies, for example Google Cloud Certified - Professional Data Engineer